intelligent detection and classification of lung nodules
TRANSCRIPT
i
Intelligent Detection and Classification of Lung
Nodules from CT Images
By
Syed Muhammad Naqi
CIIT/FA14-PCS-001/WAH
PhD Thesis
In
Computer Science
COMSATS University Islamabad
Wah Campus, Pakistan
Spring, 2019
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COMSATS University Islamabad
Intelligent Detection and Classification of Lung
Nodules from CT Images
A Thesis Presented to
COMSATS University Islamabad, Wah Campus
In partial fulfillment
of the requirements for the degree of
PhD (Computer Science)
By
Syed Muhammad Naqi
CIIT/FA14-PCS-001/WAH
Spring, 2019
iii
Intelligent Detection and Classification of Lung
Nodules from CT Images
A Post Graduate Thesis submitted to the Department of Computer Science as
partial fulfillment of the requirements for the award of Degree of Ph.D. in
Computer Science.
Name Registration No.
Syed Muhammad Naqi CIIT/FA14-PCS-001/WAH
Supervisor
Dr. Muhammad Sharif
Associate Professor
Department of Computer Science
COMSATS University Islamabad (CUI)
Wah Campus, Pakistan
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DEDICATION
To My Parents, Wife and Children
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ACKNOWLEDGEMENTS
I have completed this research thesis with the help of Almighty Allah who always
helped me whenever I was in trouble. I would like to acknowledge my advisor Dr.
Muhammad Sharif for his kind guidance and supervision that enabled me to complete
this research work. His continuous motivation, research ideas, and wonderful
knowledge helped me in a great deal to produce quality research.
I am thankful to the Head of the Computer Science Department, CUI Wah, Dr.
Muhammad Wasif Nisar who always encouraged me during the whole period of my
Ph.D. studies. I am also grateful to supervisory committee members and faculty
members of the CS department for their guidance. I am also thankful to the Higher
Education Commission (HEC), Pakistan for financial support under the Indigenous
PhD scholarship program.
I would like to extend my gratitude to the faculty of Quaid-i-Azam University. I wish
to extend my thanks to Dr. Khalid Saleem, Dr. Muhammad Tauqeer, Dr. Muddassar
Azam Sindhu, Dr. Muhammad Usman, Dr. Ayaz Hussain and Dr. Arfan Jaffar for
their valuable comments and guidance on my research.
I am especially indebted to Dr. Zahid Noman additional medical superintendent, Dr.
Naushaba Malik senior radiologist, and Dr. Maryam Rauf radiologist, at Social
Security Hospital, Islamabad; for their assistance and feedback.
Finally, a special word of thanks to my father, whose prayers not only enabled me to
complete this thesis but have been a source of illumination all my life. I want to pay
my superior appreciations to my wife, my lovely kids, and siblings for their special
prayers encouragements and patience. Thanks to every person who helped me directly
or indirectly during my PhD.
Syed Muhammad Naqi
CIIT/FA14-PCS-001/WAH
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ABSTRACT
Intelligent Detection and Classification of Lung Nodules from
CT Images
Lung cancer is considered as the most probable cancer in human beings. Moreover,
the survival rate of its patients is also very low. However, its early detection increases
the chances of survival of its patients. Medical imaging helps in early diagnosis, and
the most effective method is Computed Tomography (CT). The initial appearance of
lung cancer is the presence of a nodule in the lungs. On a CT scan, a lung nodule
appears as a round object, either independent (isolated), pleura-attached (juxta-
pleural) or vessel-attached (juxta-vascular). Computer-based systems help the
radiologists in the diagnosis process by providing the second opinion. However, there
is a significant challenge of sensitivity and the number of false positives in the
existing techniques.
The main objective of this research is to develop algorithms/methods which detect
and classify lung nodules precisely. Four methods are proposed, involving lung
volume extraction, nodule candidate detection, feature extraction, and classification.
Lung volume extraction is computed by using the optimal threshold. Fractional order
Darwinian particle swarm optimization is applied for threshold selection, which
significantly improves the segmentation of lung volume. Nodule candidate detection
is performed by applying 3D image analysis. Precise boundary correction and 3D
segmentation are proposed for the detection of juxta-pleural and juxta-vascular
nodules, respectively. Nodule candidates are refined using their shape properties. A
set of 2D, as well as 3D features, including geometric, texture, and gradient features,
are extracted with variations. Hybrid feature vectors are created by the combination
and selection of relevant features to improve classification. The initial classification is
performed by k-NN, SVM, Naïve Bayes and AdaBoost classifiers. In addition, an
SVM-ensemble classifier is implemented. Furthermore, a stacked autoencoder and
softmax is applied to reduce the false positives.
The proposed methods are evaluated over publicly available LIDC dataset, and the
largest benchmark dataset LIDC-IDRI. The first method achieved an accuracy of
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98.8%, 97.8% sensitivity and 3.7 False Positives (FP) per scan. The second method
achieved an accuracy of 99.2% with 98.3% sensitivity, 98.0% specificity and 3.3
FPs/scan. The third method achieved 99.0% accuracy, 98.6% sensitivity, 98.2%
specificity with 3.4 FPs/scan. The fourth method achieved 96.9% accuracy, 95.6%
sensitivity, 97.0% specificity and very low false positives of 2.8/scan. The results are
compared with the existing methods over standard parameters, including sensitivity,
specificity, accuracy and FPs/scan. The comparison is also extended to other
parameters, including the number of scans, number of nodules and size of nodules
used for experimentation. The comparison points to the significance of this research.
The methods presented in this thesis will be useful for the radiologists in automated
diagnosis of lung nodules at the earlier stage.
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TABLE OF CONTENTS
Introduction ................................................................................. 1 Chapter 1
1.1 Introduction ........................................................................................ 2
1.2 Lung Cancer ....................................................................................... 2
1.3 Lung Nodule ....................................................................................... 5
1.4 Computed Tomography ..................................................................... 5
1.5 CT Datasets ........................................................................................ 7
1.5.1 Lung Image Database Consortium (LIDC) ........................... 7
1.5.2 Lung Image Database Consortium-Image Database Resource
Initiative (LIDC-IDRI) .......................................................... 8
1.6 Evaluation Parameters ........................................................................ 8
1.7 Problem Statement ............................................................................. 9
1.8 Motivation and Objectives ................................................................. 9
1.9 Research Challenges ........................................................................ 10
1.10 Contributions .................................................................................... 10
1.11 Thesis Organization ......................................................................... 12
Literature Review ...................................................................... 14 Chapter 2
2.1 Introduction ...................................................................................... 15
2.1.1 Lung Region Segmentation ................................................. 16
2.1.2 Nodule Candidate Detection ............................................... 16
2.1.3 Feature Extraction ............................................................... 17
2.1.4 Classification ....................................................................... 17
2.2 Recent Developments in Lung Nodule Diagnosis ........................... 17
2.2.1 Supervised Learning Methods ............................................. 22
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2.2.2 Unsupervised Learning Methods ......................................... 25
2.2.3 Diagnosis Based on Template Matching ............................. 26
2.2.4 Diagnosis Based on Fuzzy Logic ........................................ 27
2.2.5 Diagnosis Based on Thresholding ....................................... 27
2.2.6 Diagnosis Based on Mathematical Morphology ................. 28
2.2.7 Multiple Techniques/Hybrid Approaches ........................... 29
2.2.8 Deep Learning Based Methods ........................................... 31
2.3 Discussion and Analysis .................................................................. 32
2.4 Summary .......................................................................................... 35
Proposed Methods ..................................................................... 37 Chapter 3
3.1 Introduction ...................................................................................... 38
3.2 Method I: Lung Nodule Detection using Polygon Approximation
and Hybrid Features from Lung CT Images .................................... 40
3.2.1 Lung Region Extraction using Optimal Threshold ............. 41
3.2.2 Nodule Candidate Selection using Polygon Approximation
............................................................................................. 43
3.2.3 Nodule Candidate Enhancement ......................................... 44
3.2.4 False Positive Reduction using Nodule Properties.............. 44
3.2.5 Hybrid Intensity-Geometric and Gradient Feature Extraction
............................................................................................. 45
3.2.6 Support Vector Machine for Classification ......................... 48
3.3 Method II: 3D Nodule Candidate Detection Supported by Hybrid
Features to Reduce False Positives .................................................. 48
3.3.1 Lung Volume Extraction using Optimal Threshold ............ 50
3.3.2 Nodule Candidate Detection ............................................... 52
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3.3.3 Feature Extraction ............................................................... 56
3.3.4 Classification ....................................................................... 60
3.4 Method III: Multistage Segmentation Model and SVM-Ensemble for
Precise Lung Nodule Detection ....................................................... 61
3.4.1 Nodule Segmentation .......................................................... 62
3.4.2 Geometric Texture Feature Descriptor (GTFD) .................. 68
3.4.3 Ensemble Classification ...................................................... 69
3.5 Method IV: Lung Nodule Detection and Classification based on
Geometric Fit in Parametric Form and Deep Learning ................... 70
3.5.1 Lung Volume Segmentation ................................................ 72
3.5.2 Nodule Candidate Detection using Geometric Fit in
Parametric Form .................................................................. 79
3.5.3 Feature Extraction ............................................................... 82
3.5.4 Stacked Sparse Autoencoder and Softmax Based Feature
Optimization and Classification .......................................... 85
3.6 Summary .......................................................................................... 88
Results and Discussion .............................................................. 89 Chapter 4
4.1 Introduction ...................................................................................... 90
4.2 Method I: Lung Nodule Detection using Polygon Approximation
and Hybrid Features from Lung CT Images .................................... 91
4.2.1 Experimentation and Results ............................................... 91
4.2.2 Comparison of Proposed Technique with Existing
Techniques ........................................................................... 93
4.3 Method II: 3D Nodule Candidate Detection Supported by Hybrid
Features to Reduce False Positives .................................................. 96
4.3.1 Dataset and Evaluation Parameters ..................................... 96
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4.3.2 Experimentation .................................................................. 97
4.3.3 Comparison of Proposed Method with Existing Techniques
........................................................................................... 104
4.4 Method III: Multistage Segmentation Model and SVM-Ensemble for
Precise Lung Nodule Detection ..................................................... 106
4.4.1 Experimentation and Results ............................................. 107
4.4.2 Comparative Analysis ....................................................... 111
4.5 Method IV: Lung Nodule Detection and Classification based on
Geometric Fit in Parametric Form and Deep Learning ................. 113
4.5.1 Dataset Selection ............................................................... 114
4.5.2 Segmentation Results ........................................................ 114
4.5.3 Initial Candidate Detection Results ................................... 119
4.5.4 Classification Results ........................................................ 119
4.5.5 Comparative Analysis ....................................................... 123
4.6 Summary ........................................................................................ 125
Conclusion and Future Work ................................................ 126 Chapter 5
5.1 Conclusion ...................................................................................... 127
5.2 Future Work ................................................................................... 130
References ................................................................................................... 131
List of Publications ........................................................................................ 155
Appendix: WHO Cancer Statistics .............................................................. 156
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LIST OF TABLES
Table 2.1: Categorization of lung nodule detection methods ...................................... 18
Table 2.2: Limitations in recently developed methods ................................................ 33
Table 3.1: Threshold values for nodule candidate set refinement ............................... 81
Table 4.1: Results produced by SVM linear kernel ..................................................... 91
Table 4.2: Results produced by SVM polynomial kernel ............................................ 92
Table 4.3: Results produced by SVM-RBF ................................................................. 92
Table 4.4: Best results by SVM classifier with different kernels ................................ 92
Table 4.5: Comparison of the proposed method with the results of existing techniques
...................................................................................................................................... 94
Table 4.6: Results with the geometric feature vector .................................................. 97
Table 4.7: Results with the texture feature vector ....................................................... 98
Table 4.8: Results with HOG-PCA feature vector....................................................... 98
Table 4.9: Results with texture + geometric feature vector ......................................... 99
Table 4.10: Results with texture + HOG-PCA feature vector ..................................... 99
Table 4.11: Results with geometric + HOG-PCA feature vector .............................. 100
Table 4.12: Results with geometric + texture + HOG-PCA feature vector ............... 100
Table 4.13: Results comparison of the proposed method with existing techniques .. 105
Table 4.14: Results with geometric features .............................................................. 107
Table 4.15: Results with texture features ................................................................... 108
Table 4.16: Results with GTFD ................................................................................. 108
Table 4.17: Comparison of datasets used in existing and proposed systems............. 111
Table 4.18: Comparison of results with existing methods ......................................... 113
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Table 4.19: FODPSO initialization parameters ......................................................... 114
Table 4.20: Comparison of segmentation results ....................................................... 116
Table 4.21: Results with different variations of deep net .......................................... 121
Table 4.22: Comparison of classification results ....................................................... 121
Table 4.23: Comparative analysis in terms of dataset and results ............................. 124
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LIST OF FIGURES
Fig 1.1: WHO worldwide cancer statistics 2018 [11] (a) number of new cases (b)
number of deaths ............................................................................................................ 3
Fig 1.2: WHO cancer statistics of Pakistan [12] (a) 2018 new cases (b) number of
deaths ............................................................................................................................. 4
Fig 1.3: Normal vs. nodule cells [14] (a) Normal cells (b) Cells forming a nodule ...... 5
Fig 1.4: Lung CT images with nodules (a) isolated (b) juxta-pleural (c) juxta-vascular
........................................................................................................................................ 6
Fig 1.5: Block diagram of thesis organization ............................................................. 13
Fig 2.1: Basic steps of a diagnostic system.................................................................. 15
Fig 2.2: Labeled CT slice ............................................................................................. 16
Fig 3.1: Block diagram of proposed methods .............................................................. 39
Fig 3.2: Major steps of the proposed method .............................................................. 40
Fig 3.3: Lung region extraction process ...................................................................... 42
Fig 3.4: Gray level distribution (a) input CT images (b) histogram of images (c)
threshold images .......................................................................................................... 42
Fig 3.5: Nodule candidate selection (a) objects in ROI (b) marked by the polygon ... 43
Fig 3.6: ROI and nodule segmentation (a) input CT image (b) lung mask (c) ROI with
vessels and nodules (d) nodule candidate region ......................................................... 45
Fig 3.7: Gradient orientation (a) nodule candidates (b) HOG descriptor .................... 47
Fig 3.8: Major steps of the proposed method .............................................................. 50
Fig 3.9: Lung region extraction (a) input CT images (b) histogram of images (c) final
lung masks of the images ............................................................................................. 51
Fig 3.10: Nodule candidate detection process ............................................................. 52
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Fig 3.11: Nodule candidate detection (a) segmented ROIs, (b) corresponding contours
of the objects within the ROI‘s, (c) the final selected candidates. ............................... 55
Fig 3.12: Feature extraction and selection ................................................................... 59
Fig 3.13: Workflow of the proposed method ............................................................... 62
Fig 3.14: Gray level distribution (a) input CT images (b) their respective histograms
...................................................................................................................................... 64
Fig 3.15: Juxta-pleural nodule extraction (a) input images with juxta-pleural nodules,
(b) corresponding lung masks missing pleural nodules, (c) boundary correction using
morphology, (d) extracted lungs with juxta-pleural nodules ....................................... 65
Fig 3.16: Lung region extraction (a) input image, (b) background removed, (c) lung
mask, (d) extracted lung ............................................................................................... 66
Fig 3.17: 3D connectivity of candidate pixels ............................................................. 67
Fig 3.18: Nodule candidate refinement (a) objects in ROI (b) nodule candidate regions
...................................................................................................................................... 68
Fig 3.19: Extracted nodule candidates ......................................................................... 68
Fig 3.20: Workflow of SVM-ensemble for nodule classification................................ 70
Fig 3.21: Block diagram of the proposed method........................................................ 72
Fig 3.22: Proposed lungs segmentation method .......................................................... 73
Fig 3.23: Lung region extraction (a) input CT images (b) threshold images (c)
extracted lung regions. ................................................................................................. 77
Fig 3.24: Juxta-pleural nodule extraction (a) Images with pleura nodule, (b) initial
lung mask, (c) after boundary correction, (d) final ROI .............................................. 78
Fig 3.25: Nodule candidate refinement (a) segmented ROIs, (b) the final candidate
regions .......................................................................................................................... 82
Fig 3.26: Nodule candidates (a) nodules (b) non-nodules ........................................... 82
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Fig 3.27: Texture special information (a) pixels with neighborhood directions (b) 3D
neighborhoods .............................................................................................................. 83
Fig 3.28: Feature fusion for the hybrid feature vector ................................................. 85
Fig 3.29: Stacked autoencoder for feature optimization .............................................. 87
Fig 4.1: Experimentation statistics of all proposed methods ....................................... 90
Fig 4.2: Comparison charts of classification results .................................................... 93
Fig 4.3: Comparison chart of proposed and existing system results............................ 95
Fig 4.4: ROC analysis (a) AdaBoost results with different number of iterations over
V7 (b) SVM results over hybrid feature vector V7 ................................................... 102
Fig 4.5: ROC analysis (a) results using feature vector V7 (b) results using feature
vector V5 .................................................................................................................... 103
Fig 4.6: ROC curve with GTFD feature vector ......................................................... 109
Fig 4.7: FROC analysis (a) FROC curve shows false positive reduction by each
classifier, (b) FROC curve illustrates the sensitivity comparison of each classifier at
3.4 FPs/scan ............................................................................................................... 110
Fig 4.8: 3D lung volume (a) left view and (b) right view .......................................... 117
Fig 4.9: Extracted 3D ROI with nodules and vessels (a) normal view of the complete
lung (b) a portion containing juxta-vascular nodule ................................................. 118
Fig 4.10: The first layer of autoencoder..................................................................... 120
Fig 4.11: Stacked autoencoder and softmax for feature reduction and classification 120
Fig 4.12: FROC analysis (a) FROC curve showing the performance of each classifier,
(b) FROC curve comparing the performance of classifiers at 2.8 FPs/scan .............. 122
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LIST OF ABBREVIATIONS
ACM Active Contour Models
AHE Adaptive Histogram Equalization
ANN Artificial Neural Network
AUC Area Under Curve
CLAHE Contrast Limited Adaptive Histogram Equalization
CNN Convolutional Neural Network
CT Computed Tomography
CV Chan–Vese
DCT Discrete Cosine Transformation
DPSO Darwinian Particle Swarm Optimization
DTCNN Discrete Time Cellular Neural Network
FC Fuzzy Connectedness
FCM Fuzzy C-Means
FLD Fisher Linear Discriminant
FN False Negative
FNR False Negative Rate
FODPSO Fractional Order Darwinian Particle Swarm Optimization
FOPSO Fractional Order Particle Swarm Optimization
FP False Positive
FROC Free-Response Operator Characteristic
FPR False Positive Rate
GA Genetic Algorithm
GATM Genetic Algorithm Template Matching
G-CNN Genetic Cellular Neural Networks
GLCM Gray Level Co-occurrence Matrix
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GLMR Generalized Linear Model Regression
GMM Gaussian Mixture Model
GPU Graphics Processing Unit
GTFD Geometric Texture Features Descriptor
GTSDM Gray Tone Spatial Dependence Matrices
HGTF Hybrid Geometric Texture Feature
HOG Histograms of Oriented Gradients
HU Hounsfield Unit
KNN K-Nearest Neighbor
kV Kilo Voltage
LDA Linear Discriminant Analysis
LDCT Low Dose Computed Tomography
LIDC Lung Image Database Consortium-Image
LIDC-IDRI Lung Image Database Consortium-Image Database Resource
Initiative
LoB Laplacian of Bilateral
LWTM Lung Wall Template Matching
mA.s Milli-Ampere Seconds
MRI Magnetic Resonance Imaging
MSM Mass–Spring Model
NBIA National Biomedical Imaging Archive
PCA Principle Component Analysis
PET Positron Emission Tomography
PRI Probabilistic Random Index
PSO Particle Swarm Optimization
RBF Radial Base Function
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ROC Receiver Operator Characteristic
ROI Region of Interest
SA Stacked Autoencoder
SOM Self-Organization Map
SVM Support Vector Machine
TN True Negative
TP True Positive
VoI Variation of Information
WHO World Health Organization
XML Extensible Markup Language
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LIST OF SYMBOLS
Threshold
Coordinate x-axis
Coordinate y-axis
Previous bias change
Momentum constant
Derivative of the performance in the weight
Attribute in the feature vector
Target class
Width of the polygon surrounding an object
Length of the polygon
ø Empty set
Set of objects in ROI
An object in set S
Probability of class
Probability of a gray level in gray level co-occurrence matrix
Mean
Standard deviation
Skewness
E Energy
H Entropy
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Kurtosis
Maximum gray level in an image
Mean of class
Area
Diameter
Radius
Set of voxels in the segmented object
Voxel
Position of snake
Data vector of example in the data
Class label of example in data
Bias
Regularization parameter in SVM
Slack variable to control the margin in SVM
Weight Vector
Degree of SVM polynomial kernel
Gaussian of standard deviation
Ratio threshold
Lower threshold of area
Higher threshold of area
Circularity threshold
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Standard deviation with x-axis
Standard deviation with y-axis
Gradient magnitude with x-axis
Gradient magnitude with y-axis
Lowest gray level in an image
Highest gray level in an image
Geometric features
Texture features
HOG features
Serial fusion
Target class
Euclidian Distance
Segment length of lung boundary
Union
Probability distribution
Number of gray levels
Histogram of gray level g
Gray level in an image
Source image
Binary image
Gradient operator
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The scalar to control elastic energy in ACM
Trust factor
The scalar to control bending energy in ACM
The fractional coefficient in FODPSO
Learning rate
Additional learning rate
Partial derivative
Global weight controller
Local weight controller
The penalty associated with the probability of a category
Maximum threshold for the diameter of the nodule
Total iterations in FODPSO
Total swarms
Minimum swarms
Total particles in each swarm
Minimum particles in each swarm
Maximum particles in each swarm
Maximum swarms
Total pixels
1
Chapter 1
Introduction
2
1.1 Introduction
This thesis focus focuses on the detection and classification of lung nodules. Lung
cancer detection at initial stage increases the survival rate of patients. Automatic
detection of lung nodules facilitates radiologists during the diagnosis process.
However, there is a challenge of false positives in automated systems which may lead
to wrong findings. The primary focus of this study is the reduction of false positives
by preserving sensitivity.
1.2 Lung Cancer
In the 20th
century, more than 100 million deaths were reported due to tobacco use. It
is expected that the deaths might reach 1 billion in the 21st century. About one-third of
these deaths are due to lung cancer [1, 2]. Moreover, the lung cancer mortality rate in
comparison to other types of cancers is very high [3]. According to the statistics, 1.76
million deaths and 2.09 million new cases are reported in 2018, which is the highest
number in all types of cancers [4] as shown in Fig 1.1. In Pakistan, lung cancer is the
third largest cancer. According to WHO, there are 9771 new cases reported in
Pakistan during 2018, as shown in Fig 1.2. The initial appearance of lung cancer is the
presence of a nodule in the lungs. Different types of tests are usually advised by
doctors to identify lung cancer. More important of those tests is screening using
radiography, which includes Computed Tomography (CT) [5], Magnetic Resonance
Imaging (MRI) [6], and Positron Emission Tomography (PET) [7]. The CT scan is the
most common and cost-effective method to identify lung cancer [8]. Abnormalities
identified through CT scan imply the possibility of lung cancer in an individual.
During the CT scan of a patient, more than one hundred images are generated.
Radiologists have to examine all the images to identify cancer, which is very time-
consuming and error-prone. To overcome this issue, it is much needed to have some
automated or semi-automated systems which help the radiologists to distinguish
cancerous and non-cancerous regions clearly. In medical imaging, automated decision
support systems have a key role in early diagnosis [9, 10]. These systems facilitate the
radiologists by providing the second opinion. Radiologists use such decision support
systems to identify cancerous regions in the CT scan images. These systems comprise
multiple computer vision algorithms.
3
(a)
(b)
Fig 1.1: WHO worldwide cancer statistics 2018 [11] (a) number of new cases (b)
number of deaths
4
(a)
(b)
Fig 1.2: WHO cancer statistics of Pakistan [12] (a) 2018 new cases (b) number of deaths
5
1.3 Lung Nodule
Human cells grow in a well-controlled manner. In normal growth, the cells follow a
regular pattern of dividing and dying to produce a few new cells. The uncontrolled
growth of these cells leads to cancer. These abnormal cells grow very fast in number
and form a lump inside the lungs, also called lung nodule [13]. Fig 1.3 shows the
normal and abnormal cell pattern.
Fig 1.3: Normal vs. nodule cells [14] (a) Normal cells (b) Cells forming a nodule
A small size lung nodule is also called pulmonary nodule [15]. It is a round shaped
object within the lungs with a diameter less than 30mm. A nodule can be malignant or
benign. Benign nodules are non-cancerous, whereas malignant nodules are cancerous
[16]. Malignant nodules are harmful, and they grow with the passage of time.
Treatment of the patient becomes much harder if size of the nodule becomes large. In
addition, a nodule is also categorized depending upon its position within the lung. It
can be isolated [17], attached to the boundary of the lung called juxta-pleural nodule
[18] and vessel attached (juxta-vascular) nodules [19]. Different types of nodules in a
CT image are shown in Fig 1.4. Larger nodule size can disturb the normal functioning
of the lungs and can result in death in a very short span of time [20]. If the size of a
nodule becomes larger, then treatment of the patient becomes harder [21]. Therefore,
it is more desirable and highly recommended that cancer should be identified at its
initial stage.
1.4 Computed Tomography
Computed tomography (CT) was invented by a Nobel laureate Dr. G. N. Hounsfield
in 1971 [22]. CT is one of the most common procedures to achieve a non-invasive
visualization of the body's internal structure. CT images are produced by combining
(a) (b)
6
X-ray technology and a computer to help doctors to visualize the body in depth and
examine the organs. An X-ray tube slowly rotates 360° around the human body and
takes pictures. These images are called slices. If a suspicious structure is found during
a normal radiograph or ultrasound scan, the doctor may suggest a CT scan of that part
of the body. It is a method to investigate the internal structure of the human body in
detail; it enables doctors to check for any internal bleeding, mass, brain cancer, or
lung nodule. Over the last two decades, it has become a standard imaging modality.
CT produces more precise results for bone structures. However, it is also a preferred
technique for brain and lung cancer diagnosis [23]. Sometimes a CT scan performed
on the basis of other symptoms can result in detection of smaller nodules such as in
the case of known carcinoma of lung or anywhere else. The physician looking at a
metastatic disease can result in the detection of a nodule.
(a)
(b)
(c)
Fig 1.4: Lung CT images with nodules (a) isolated (b) juxta-pleural (c) juxta-vascular
7
The actual scan time for CT is usually less than for an MRI or PET scan, and it is less
sensitive to patient movement. Moreover, it is more cost effective than both [24]. It is
extremely sensitive, detecting nodules as small as 3mm [25, 26], that are invisible on
a conventional X-ray examination of the lungs. In fact, recent research on CT
screening reveals that most cases of lung cancer detected in CT are invisible on a
conventional chest X-ray carried out at the same time as the CT scan. However, the
recommendation is to use Low Dose Computed Tomography (LDCT) as even this can
identify nodules smaller than can conventional chest radiography due to
advancements in CT [27, 28].
1.5 CT Datasets
A well-characterized repository plays a vital role in the performance evaluation of a
diagnosis system. A diagnosis system produces the best performance when it is
trained over a large amount of well-documented data. Some researchers used clinical
data from different hospitals, which is a very time-consuming and intensive process.
Extra work is required to normalize these images. Therefore, it is necessary to
develop a standard repository that needs a lot of human resources and time [29]. In
recent years, various research groups have been working to resolve this issue for the
betterment of the diagnosis system performance, which can reduce the risk of false
positives [30].
1.5.1 Lung Image Database Consortium (LIDC)
LIDC is a standard dataset of lung CT images that is publicly available to the
researchers for the performance evaluation of their proposed methods. It can be
accessed through the National Cancer Institute's Imaging Archive [31-33]. This
database of thoracic CT images is developed with the cooperation of five medical
imaging research groups. The motivation behind the development of LIDC was to
develop a database of CT images as a web-accessible resource for the evaluation of
diagnosis systems [34]. Four experienced radiologists of different institutions
performed the annotations of the nodules with reference to their position and size. The
nodules in this dataset are of sizes ranging from 3-30mm [35].
8
1.5.2 Lung Image Database Consortium-Image Database Resource
Initiative (LIDC-IDRI)
LIDC-IDRI is the extended version of LIDC dataset. Initially, the LIDC consisted of
84 scans containing 58 nodules. In 2004 it was combined with Image Database
Resource Initiative (IDRI). With the passage of time size of the database has been
extended up to 1012 scans to form LIDC-IDRI. It is the largest and benchmark
database that is publicly available to support researchers in the evaluation of diagnosis
systems for pulmonary nodules. Moreover, along with the image data, another
repository of XML files is also provided for the convenience of researchers to get
annotation details about the nodules present in the dataset. In recent years, this dataset
has been considered as a benchmark dataset and is the most widely used in the
performance evaluation of diagnosis methods [36].
1.6 Evaluation Parameters
A diagnosis system classifies the images into diseased and normal categories. The
confusion matrix [37] is used to evaluate the performance of diagnosis methods,
which consists of four possible outcomes. If a positive instance of test data is
correctly classified by the diagnosis method, then it is called True Positive (TP),
otherwise False Negative (FN). Similarly, if a negative instance is classified as
negative, then it is referred to as True Negative (TN) otherwise False Positive (FP).
The specificity, sensitivity [38] and accuracy are used as performance measures [14]
of the proposed diagnosis methods. These measures are given in Eq. 1.1 to Eq. 1.3.
(1.1)
(1.2)
(1.3)
The sensitivity represents the true positive rate, and specificity shows the true
negative rate. In addition to these parameters, Receiver Operator Characteristic (ROC)
analysis is important and is a widely used measure [37, 38]. It is created by plotting
9
false positive rate versus sensitivity, and the Area Under the ROC Curve (AUC) is
computed. Its value ranges from 0 to 1, where 1 represents the ideal performance. In
addition to the ROC curve, another important measure named Free-Response
Operating Characteristic (FROC) analysis [39] is often used to validate the
performance of lung nodule diagnosis methods. It is created by plotting sensitivity
versus the average number of FPs/scan. In a single CT scan, some images have more
than one nodule, and some may not have any. Therefore, FROC measure is an
important measure that shows the relationship of sensitivity with the number of FPs
per scan. Although, the most recent studies have presented various lung nodule
detection and classification methods, but to overcome the number of false positives
remains a challenge.
1.7 Problem Statement
Existing methods lack both in achieving high accuracy and low false positive rate. It
is due to limitations in handling the complex structure of the lungs, and a large
number of CT images generated during a CT scan. Existing methods also lack in
handling different kinds of nodules, including isolated, juxta-pleural and juxta-
vascular nodules, especially of smaller sizes ranging from 3-30mm. The proposed
research aims to address these problems by providing novel methods for detection and
classification of nodules using image processing and machine learning techniques.
1.8 Motivation and Objectives
The rapid increase in lung cancer is an alarming situation. According to the statistics
of WHO, lung cancer is the leading type of cancer for the last two years. Therefore, it
is very important to diagnose lung cancer at its earliest stage. Lung nodule diagnosis
methods still have a challenge of false positives. The major focus of this research is
on lung CT image processing. The main objective is to develop
algorithms/techniques:
To automatically identify nodular regions in CT images.
To view CT images of patients and improve the performance of the diagnostic
methods in terms of sensitivity and the number of false positives.
To ensure the inclusion of juxta-pleural nodules in the ROI during the
segmentation. It improves the accuracy of the diagnosis process.
10
To separate the juxta-vascular nodules from vessels precisely for better
performance of the diagnosis method.
To segregate actual nodules from potential nodules by using relevant feature
extraction and robust classification.
To facilitate the radiologists during the diagnosis process by providing the
second opinion.
1.9 Research Challenges
As discussed earlier, the existing diagnosis methods lack both in high accuracy and
low false positive rate. Major contributory factors are limitations in the handling of
different types of nodules. Major challenges in automated diagnosis of lung nodules
are listed below:
Precise segmentation of juxta-pleural nodules is a challenging problem.
During the diagnosis process, a fundamental step is the extraction of the lung
portion. During this process, juxta-pleural nodules are fully or partially
removed from ROI due to their presence on the boundary of lung.
The separation of juxta-vascular nodules from vessels involves another
challenge. Current diagnosis methods usually miss juxta-vascular nodules
during vessel removal, and this results in a wrong diagnosis.
Detection of small nodules is a challenge as it contributes to false positive rate.
Instead of using 2D slices, a 3D approach can produce better results. However,
dealing with all slices of a scan during the process of diagnosis is another
challenge.
A major challenge for this study is to diagnose abnormal areas with fewer
false positives. To do so, there is a need to classify nodules and non-nodules
with greater classification accuracy using novel segmentation and
classification techniques.
1.10 Contributions
The major contributions of this research, which added to the existing body of
knowledge in this area are as follows:
11
Nodule candidate detection is a critical step that impacts the performance of a
diagnosis method. Lung nodules are of three types with reference to their
presence in the lung area, including juxta-pleural, juxta-vascular and isolated
nodules. These nodules cannot be detected with a single straight forward
method. To address this challenge, a multistage segmentation method is
proposed in this thesis to extract the lung volume and nodule candidates
precisely, which was lacking in the existing systems. It improved the detection
of actual nodules and reduced the inclusion of false positives in the initial
candidate set. The nodule candidate set is further refined by applying
geometric fit in parametric form
The segmentation of juxta-vascular nodules impacts the performance of a
diagnosis method. It is challenging to separate these nodules due to their
attachment to the complex structure of vessels. In this thesis, a novel 3D
segmentation of juxta-vascular nodules from vessels is proposed. Existing
systems perform slice-level segmentation and then reconstruct the 3D nodule
structure by analyzing the information. In contrast, this thesis proposes a scan
level 3D segmentation method, which reduces the number of false positives
and improves overall performance.
Lung region extraction is an important phase of the diagnosis process. A fixed
threshold cannot perform precise segmentation of all slices of a CT scan due
to heterogeneity of the slices in a CT scan. It results in missing some juxta-
pleural nodules and the inclusion of some extra regions with the lung area,
which results in the wrong diagnosis. To overcome this challenge, there is a
need to compute the optimal threshold with some strong optimization
algorithm. In meeting this bar, a FODPSO based lung volume extraction
method is proposed for accurate segmentation of the lung area.
SVM-ensemble classification based on three base classifiers linear,
polynomial and RBF is developed for better classification of nodules and non-
nodules. Instead of using the same type of base classifiers that may follow a
similar pattern, heterogeneous base classifiers are combined to improve the
classifier performance.
A stacked autoencoder and softmax based deep learning approach is proposed
for feature reduction and classification. We have designed a two-level stacked
12
autoencoder for feature optimization. It leads to an improved classification of
nodules.
Detailed texture and shape analysis is performed to improve the classification
performance. Furthermore, for texture analysis, Gray Tone Spatial
Dependence Matrices (GTSDM) is computed based on different angular ratios
and distances among the neighborhood pixels in 2D as well as 3D. The
analysis is extended by computing the mean and range of each feature.
Intensive experimentation is performed by creating different combinations of
the feature vectors. Such detailed level analysis was lacking in the existing
methods. As a result, the proposed diagnosis methods outperformed the
existing methods.
1.11 Thesis Organization
This thesis consists of five chapters, including this chapter. Fig 1.5 depicts the
organization of the thesis hierarchy. The chapters are organized as follows:
Chapter 1 covers a brief introduction to lung cancer, its statistics and the need for
automated diagnosis systems. It also describes the key objectives, motivation and
research challenges.
Chapter 2 reviews the recently developed lung nodule diagnosis methods proposed in
recent years. This review provides details about various image analysis and machine
intelligence methods used in the literature for lung nodule detection and classification.
Chapter 3 gives the details of the proposed methods designed to overcome the
limitations discussed in the previous chapter. Four methods are presented for the
precise detection and classification of nodules. Their main focus is to improve the
diagnosis process by reducing the number of false positives while retaining high
sensitivity.
Chapter 4 illustrates the details of experimentations, datasets, and results. It also
provides a detailed comparative analysis of the proposed methods with existing
methods reported in the literature.
Chapter 5 concludes the thesis by providing a summary of the thesis contributions
and future directions of research.
13
Fig 1.5: Block diagram of thesis organization
14
Chapter 2
Literature Review
15
2.1 Introduction
During the last decade, many researchers performed baseline studies for the
development of modern IT systems to identify and characterize nodules by processing
the radiographs based on computer vision and machine intelligent algorithms. These
kinds of systems are more supportive to the radiologists in terms of time and
correctness [40].
Fig 2.1: Basic steps of a diagnostic system
Computer-based systems were initiated in 1980 at the University of Chicago [41]. The
basic aim for such applications was to provide a computer-based system with
improved reliability and consistency to the radiologist when interpreting CT images.
The method must have also been time-efficient. It was a key issue from the early
1960s, but the success rate was very low in the development of an automated
computer-based system to assist the radiologists during the examination of a patient‘s
images [42, 43]. Fig 2.1 shows the fundamental steps of a diagnostic system, which is
a four-step process. In the first step, the input image is divided into a set of regions
called segments that lead to separation of lung parenchyma as well as internal details
of the lung volume, also known as Region of Interest (ROI) [44]. The ROI extraction
is a very useful step because rather than processing the whole image, it is better to
process only those sections where actual nodule may be found. After lung region
segmentation, the next step is to extract the objects from ROI, which have similar
properties as of nodule. These objects are known as nodule candidates. A set of
candidate objects is created, including actual nodules and non-nodules where nodules
are diseased objects. In the third step, features are extracted from candidate objects.
Classification is the next step that uses image features to identify the nodules, and it
provides the second opinion to radiologists in diagnoses. Fig 2.2 shows a labeled CT
image that provides information about different regions within a slice.
16
Aorta
Air
Bronchi
Ribs
Middle lobe
Branching
Vertebral body
Upper lobe
Chest muscles
Parenchyma
Airway
Lower lobe
Fig 2.2: Labeled CT slice
2.1.1 Lung Region Segmentation
The precise segmentation is essential and crucial. It facilitates the determination of the
exact location of a nodule within a CT image that impacts the detection and treatment
of lung cancer [45, 46]. Segmentation partitions the image into different regions that
have homogeneous regions inside and heterogeneous to each other. Proper
segmentation makes life easier for radiologists [47]. Several segmentation methods
have been proposed including thresholding [48], template matching [49, 50], 3D
template matching [51], morphological operation [52] and watershed [53].
Thresholding is a simple and popular technique used for segmentation. Different
variants of thresholding techniques are reported in the literature, including simple
thresholding, optimal thresholding [54], Otsu thresholding [55], thresholding
combined with morphological operations [56], fuzzy thresholding [57] and multiple
thresholds [58-61]. Some researchers also used combinations of different
segmentation methods to improve the segmentation quality. The accuracy of
segmentation directly affects the subsequent steps, i.e., feature extraction and
classification of pulmonary nodules [62, 63].
2.1.2 Nodule Candidate Detection
The nodule candidate detection in the automated diagnosis process has prime
importance. This step requires the consideration of key properties of nodule
candidates to detect them accurately. The nodules are round shape circular objects of
different sizes ranging from 3mm to 30mm [64]. In a CT scan, the nodules appear to
be the brighter object with maximum intensity at the center and mostly within dark
surroundings. The main target of this stage is to perform initial candidate detection
17
with 100% sensitivity, which reflects the inclusion of all nodules in the candidate set.
However, a major issue of this step is the inclusion of a large number of non-nodules
in the candidate set, which leads to high false positives. The higher sensitivity with
low false positives depicts the strength of a detection method.
2.1.3 Feature Extraction
Image features play a vital role in object classification. An image consists of a large
set of pixel values, each having some useful and related features. A feature is a
numerical representation of an image or object within the image. The extraction of
these values is called feature extraction, and from those features identification of most
relevant values, which can better represent an image, is called feature selection. On
the basis of selected features classification of nodules and non-nodules is performed,
various machine learning classifiers are used for this purpose [65]. A poor selection of
features can lead to the wrong diagnosis. The right feature selection improves the
diagnosis system performance, which shows the effectiveness of the feature selection
and extraction process [66].
2.1.4 Classification
Classification is the final step of the diagnosis process, which distinguishes nodules
and non-nodules. A classifier is trained on labeled examples. In the testing phase,
unlabeled data is provided to the trained classifier, which classifies it to a relevant
class [67]. SVM [68], Bayesian [69], and Artificial Neural Network (ANN) [70, 71]
are widely used classifiers in the automated diagnostic systems. Many researchers
have also used multiple classifiers by combining their results to form an ensemble
classifier [72]. The classifiers are trained and tested with the features extracted from
the candidate nodules.
2.2 Recent Developments in Lung Nodule Diagnosis
Automated diagnostic systems for lung cancer have remained a key area of research
in recent years. There is a primary issue of sensitivity and false positives in these
diagnosis systems. Lung nodule diagnosis methods are mainly comprised of
segmentation, feature extraction and classification [73]. The segmentation is further
divided into lung region extraction and nodule candidate detection. Furthermore, to
evaluate the performance of a diagnosis system, a standard dataset also plays a vital
18
role. Table 2.1 gives valuable details of different methods used in automated lung
nodule diagnosis. The methods proposed in these articles are categorized by novelty
area, in this section.
Table 2.1: Categorization of lung nodule detection methods
Author Year Feature Types Classification/Detection Methods Category
Armato, et
al. [74]
1999 Geometric and gray
level features
Gray-level thresholding Linear
Discriminant Analysis (LDA)
classifier
Thresholding
Armato III,
et al. [59]
2001 Morphological
and gray level
features
Multiple gray-level thresholds Thresholding
Lee, et al.
[75]
2001 Shape and texture Genetic algorithm template
matching (GATM)
Template
Matching
Armato, et
al. [76]
2002 Geometric features Gray-level thresholding, cascaded
and rule-based classifiers
Supervised
Learning
Gurcan, et
al. [77]
2002 Morphological
features
K-means, rule-based classification,
LDA
Unsupervised
Learning
Lin and Yan
[78]
2002 Circularity, size of
the area, and mean
brightness
Neural fuzzy model Fuzzy Logic
Zhao [79] 2003 Size and compact
shape
Local density maximum algorithm
based on thresholding
Thresholding
Kostis, et al.
[80]
2003 Morphological
features
Mathematical morphology and
gray level thresholding
Morphology
Based
Bae, et al.
[81]
2005 Morphological
features
3D and 2D morphological
matching
Morphology
Based
Lin, et al.
[82]
2005 Number of pixels,
gray value, Size of
area of the region
Neural network-based fuzzy model Fuzzy Logic
Zhang, et al.
[83]
2005 Local shape features Discrete-time cellular neural
network (DTCNN), genetic
algorithm (GA)
Supervised
Learning
Boroczky, et
al. [84]
2006 Geometric features
optimized using GA
GA for feature reduction and SVM
for classification
Supervised
Learning
Dehmeshki,
et al. [49]
2007 3D geometric Shape-based GATM Template
Matching
19
Author Year Feature Types Classification/Detection Methods Category
Osman, et
al. [85]
2007 3D shape features 3-D template-matching Template
Matching
Retico, et al.
[86]
2008 Morphological
features
Dot enhancement filter and Neural
Network
Unsupervised
Learning
Ozekes, et
al. [51]
2008 Shape properties 3D template matching, fuzzy
thresholding, and G-CNN
Template
Matching
Ye, et al.
[57]
2009 3-D local geometry
and statistical
intensity features
Adaptive fuzzy thresholding
method for segmentation, SVM, a
rule-based classifier
Hybrid
Method
Murphy, et
al. [87]
2009 Local shape features kNN classifier Supervised
Learning
da Silva
Sousa, et al.
[88]
2010 Geometric features,
Texture features
Mathematical morphology,
thresholding, SVM
Hybrid
Method
Messay, et
al. [89]
2010 Geometric, intensity
and gradient features.
Fisher Linear Discriminant (FLD)
and quadratic classifiers
Supervised
Learning
Lee, et al.
[90]
2010 Gray level values Random forests based
classification
Supervised
Learning
Lee, et al.
[91]
2010 Geometric and
texture features
Ensemble classification Supervised
Learning
Chen, et al.
[92]
2011 Texture, geometric Neural network ensemble Supervised
Learning
Riccardi, et
al. [56]
2011 Geometrical features,
maximum intensity
projection
Histogram thresholding,
mathematical morphology, radial
filtering, SVM
Hybrid
Method
Magalhães
Barros
Netto, et al.
[93]
2012 Shape and texture SVM classifier Supervised
Learning
Cascio, et al.
[94]
2012 Geometric and
intensity
3D Mass-Spring Model Supervised
Learning
Chen, et al.
[95]
2012 Morphological
features
Neural network Supervised
Learning
Choi and
Choi [96]
2012 Geometric, intensity,
DCT, wavelet,
texture features
Genetic programming based
classifier
Supervised
Learning
20
Author Year Feature Types Classification/Detection Methods Category
Elizabeth, et
al. [60]
2012 Shape and texture Thresholding Thresholding
Teramoto
and Fujita
[97]
2013 Shape and intensity Thresholding, cylindrical nodule
enhancement filter
Hybrid
Method
Choi and
Choi [98]
2013 Geometric and
texture features
Thresholding method for
segmentation and SVM
Supervised
Learning
Wang, et al.
[99]
2013 Texture and shape
features
SVM based on three-dimensional
matrix patterns
Supervised
Learning
Tartar, et al.
[100]
2013 Morphological
features and patient
information
Decision tree Supervised
Learning
Keshani, et
al. [101]
2013 2D stochastic and 3D
anatomical features
Adaptive fuzzy thresholding and
active contour models (ACM)
Supervised
Learning
Jang, et al.
[102]
2013 2D and 3D features Fuzzy clustering and genetic
algorithm
Fuzzy Logic
Choi and
Choi [54]
2014 Three-dimensional
shape-based feature
Dot enhancement filtering/SVM Supervised
Learning
Kuruvilla
and
Gunavathi
[103]
2014 Statistical parameters Artificial neural network Supervised
Learning
Cao, et al.
[104]
2014 Intensity, shape, and
gradient features
Ensemble learning Supervised
Learning
de Carvalho
Filho, et al.
[105]
2014 Shape and texture Thresholding based segmentation Hybrid
Method
Badura and
Pietka [106]
2014 Texture features Fuzzy connectedness and the
evolutionary computation
Hybrid
Method
Brown, et al.
[107]
2014 Shape features Watershed, intensity thresholding
and Euclidean Distance
Transformation (EDT). A vector
quantization (VQ)
Hybrid
Method
Taşcı and
Uğur [108]
2015 Texture features Otsu thresholding, morphological
operations, generalized linear
model regression (GLMR)
classifier.
Thresholding
21
Author Year Feature Types Classification/Detection Methods Category
Akram, et
al. [109]
2015 Geometric and
intensity based
statistical features
Artificial neural network Supervised
Learning
Wang, et al.
[110]
2015 Shape features Spherical shape enhancement
filter, Chan–Vese (CV) model
Supervised
Learning
Kaya and
Can [111]
2015 Shape, size, and
texture features
Ensemble classification Supervised
Learning
Shi, et al.
[112]
2015 Gray values ROI extraction based on hessian
matrix and Laplacian of Bilateral
(LoB)
Unsupervised
Learning
Dai, et al.
[113]
2015 No features Graph cuts algorithm with
Gaussian mixture models (GMMs)
Unsupervised
Learning
Han, et al.
[114]
2015 Geometric, intensity,
gradient, and hessian
features
Different levels of vector
quantization for lung and nodule
segmentation.
Unsupervised
Learning
Shen, et al.
[115]
2015 Contextual features Bidirectional chain Code, SVM Hybrid
Method
Hua, et al.
[116] 2015 Texture and
morphological
features
Deep belief network Deep
Learning
Firmino, et
al. [117]
2016 HOG features Region growing, SVM and rule
base classifiers
Supervised
Learning
Mukhopadh
yay [58]
2016 Texture and intensity Multiple thresholds
Thresholding
Nibali, et al.
[118]
2017 Deep convolutional
features
Deep residual learning, curriculum
learning, and transfer learning
Deep
Learning
Dou, et al.
[119]
2017 Deep convolutional
features
multi-level 3D CNN Deep
Learning
da Silva, et
al. [120]
2017 Deep convolutional
features
Deep learning and genetic
algorithms.
Deep
Learning
Sun, et al.
[121]
2017 Shape, texture and
deep features
CNN, DBN, Autoencoder Deep
Learning
Gupta, et al.
[122]
2018 Shape, texture and
intensity features
Neural network classifier Supervised
Learning
22
Author Year Feature Types Classification/Detection Methods Category
Zhang, et al.
[123]
2018 Morphology based
3D skeletonization
feature, shape feature
and intensity features
ACM, thresholding, morphological
operation,, SVM
Morphology
Based
Jaffar, et al.
[124]
2018 Intensity and gradient Differential evolution based
thresholding
Hybrid
Method
Teramoto
and Fujita
[125]
2018 Shape and texture Thresholding, nodule enhancement
filter, rule based classifier, SVM
Hybrid
Method
Gong, et al.
[126]
2018 Shape, intensity and
texture features
Random forest classifier, level set
segmentation
Hybrid
Method
Ali, et al.
[127]
2018 Deep features Reinforcement learning based deep
neural network
Deep
Learning
Jiang, et al.
[128]
2018 Deep features Four channel CNN, Frangi
filter
Deep
Learning
Li, et al.
[129]
2018 Deep features CNN-ensemble Deep
Learning
Huidrom, et
al. [130]
2019 Shape, intensity and
gradient features
Neural Network optimized with
GA and PSO
Supervised
Learning
Shaukat, et
al. [131]
2019 Shape, texture and
intensity features
ANN, thresholding and watershed
segmentation
Supervised
Learning
Xie, et al.
[132]
2019 Deep features with R-
CNN
R-CNN and Boosting CNN Deep
Learning
Kasinathan,
et al. [133]
2019 Deep features ACM, enhanced AlexNet Deep
Learning
2.2.1 Supervised Learning Methods
It is a machine learning process in which a classifier is trained on a set of data with
predefined class labels. After the training process, the classifier is tested against the
test data to classify the desired patterns in the correct classes [67]. In [103]
feedforward and backpropagation neural networks were used. To train the neural
network, they used statistical features extracted from the segmented lung portion of
CT images. Thirteen predefined training functions for backpropagation network were
used in addition to two newly proposed training functions. Best accuracy was
23
achieved with backpropagation algorithm with the training function.
Another ANN, thresholding and intensity feature based method was proposed in
[109]. A 3D matrix model by taking SVM as a base classifier was applied to detect
the nodules from 1D and 2D models [99].
Chen, et al. [95] presented a diagnosis system based on neural network and logistic
regression methods. Limitation of this approach is that it was only tested on a small
dataset, and no external evaluation was done. Discrete Time Cellular Neural Network
(DTCNN) was applied to identify non-pleural nodules from helical CT images [83].
To train the classifier, local shape features were extracted. For juxta-pleural nodules
detection, morphological, thresholding and labeling were performed. After that, the
classification was applied by training Linear Discriminant Analysis (LDA) classifier
on geometric and gray level features. Murphy, et al. [87] used local shape features and
kNN classifier. The system was tested over 813 scans and 80% sensitivity with 4.2
FPs/scan.
SVM-based supervised learning was developed for lung cancer diagnosis [93]. Before
applying SVM, nodule segmentation was carried out as a preprocessing step. After
that, shape and texture features were extracted for training the classifier. Another
supervised learning genetic programming classifier [96] was developed. In the first
step, lung area was segmented from CT image using thresholding. In the second step,
multiple thresholds combined with 3D pruning were applied to identify nodule
candidates. Finally, features were extracted, and by using these features classification
is performed. The performance is evaluated over LIDC.
A three stage hierarchical block classification based diagnosis system was developed
in [98]. The 3-D CT images were divided into hierarchical blocks and by entropy
analysis method required blocks were identified. After that, the thresholding method
was used for segmentation. Three types of features were extracted from segmented
regions, including 2-D geometric, 2-D texture, and 3-D geometric features. Finally,
SVM was applied to classify pulmonary nodules.
Choi and Choi [54] proposed another lung cancer detection method based on three-
dimensional shape features. To refine the feature vector, a wall elimination method
was used. Multi-scale dot enhancement filter [98] was used to obtain lungs portion,
and SVM was used for classification.
24
An optimum feature subset selection method for improvement of the diagnosis
process was presented in Boroczky, et al. [84]. For this purpose, they used a genetic
algorithm for optimal feature selection from 2D and 3D geometric features of lung CT
images. From each structure, 23 features were selected and then ten best features with
high relevancy were extracted by using GA optimization. SVM was trained on the
selected feature subset, which achieved 100% sensitivity and 56% specificity. Chan–
Vese (CV) model [110] consisting of three steps, including segmentation, candidate
nodule extraction, and FP reduction, improved the diagnosis process.
A feature based lung nodule detection technique [89] was developed, comprising of a
feature set of shape, intensity and gradient features, extracted from segmented
images. Different subsets of these features were used in classification. A lung nodule
detection method was proposed in [101], where two segmentation techniques adaptive
fuzzy thresholding and Active Contour Models (ACM) were collectively used. SVM
was applied for nodule classification with 2D stochastic and 3D anatomical features.
An automated diagnosis system was developed to identify the nodules which might be
missed on initial CT scan visual analysis [76]. For this purpose, they used multiple
techniques, including gray-level thresholding, rule-based scheme, and a cascaded
automated classification. Thresholding was used to detect 3D structures within the CT
scan. After that, an automated analysis was done on the detected structures to identify
nodule candidates. In the third step, cascade and rule-based classifiers were used to
identify actual nodules. Stable 3D Mass-Spring Model (MSM), was proposed to
detect small sized pulmonary nodules and juxta-pleural nodules [94]. A diagnosis
system based on SVM and rule base classifiers was proposed in [117]. Region
growing was applied to extract nodule candidates, and classifiers were trained with
HOG features.
Gupta, et al. [122] presented a three phased method. In the first phase, they performed
the lung region segmentation based on thresholding approach. In the second phase,
nodule candidates were extracted using morphology. In the final step, nodule
classification was performed based on a three-layer neural network. A neural network
classifier optimized with GA and PSO was developed for nodule detection and
classification [130]. Intensity, geometric and gradient features were extracted to train
the network. A two layer ANN was trained using shape, intensity and texture features
25
to detected nodules [131]. Before classification, lung region and nodule candidates
were extracted using threshold and watershed algorithms.
Ensemble classification is another supervised learning technique that performs
classification by combining the decision of individually trained classifiers [134]. A
nodule detection method was proposed in [111] based on shape, size, texture features
and ensemble classification. An ensemble classifier based on the random forest
supported by the clustering applied for diagnosis [90]. Firstly, nodules and non-
nodules instances were clustered, and after that, by training labels, random forest
classification was applied. In this technique, clustering was combined with the
classification to achieve high accuracy. Another ensemble classification technique
based on the combination of a genetic algorithm and random subspace method to
improve the performance was proposed [91]. For the classifier training, the features
were selected in two steps. Initially, 126 geometric and texture features were selected
and divided into different subsets to find a more appropriate set of features to achieve
better performance.
A binary ensemble classifier [104] was applied to reduce the false positive rate.
Weighted voting was applied for the ensemble of classifiers. Another ensemble
learning technique was proposed using the neural network ensemble to detect nodules
from CT images [92]. To train the networks, texture and geometric features were
used. Afterwards, they combined the individual networks, and to improve the results;
they used Bayesian rule for ensemble learning. Resultant ensemble neural network
produced better results than individual networks.
2.2.2 Unsupervised Learning Methods
Segmentation is a crucial factor for lung nodule detection in a diagnosis system.
Various segmentation techniques were proposed by different researchers in the last
two decades. Unsupervised learning techniques, including k-means [135], Fuzzy c-
means (FCM) is a variation of fuzzy logic are useful clustering algorithm [136]. In
[137] FCM was used for lung image segmentation from CT of nodule from the lung
region. In this section, different unsupervised learning methods are discussed in detail.
Tolouee, et al. [138] proposed a three-stage model for high-resolution CT images,
which mainly comprised of extraction of a region of interest, feature extraction and
finally, classification to normal and abnormal lung tissue patterns. For this purpose, a
26
set of thresholding, filtering, and morphological operators were used. For feature
extraction, wavelet features were extracted and finally, fuzzy k-nearest neighborhood
classifier was used for nodule classification.
Hessian matrix and Laplacian of Bilateral (LoB) filter was developed, which extends
Laplacian of Gaussian [112], but it needs high computational time. Han, et al. [114]
proposed different levels of vector quantization for lung and nodule segmentation.
The geometric, intensity, gradient and hessian features were used for false positive
reduction.
Gurcan, et al. [77] proposed a diagnosis system in which they used k-means clustering
for nodule candidate segmentation. After that, a rule-based classifier was combined
with linear discriminant analysis (LDA) and produced good accuracy. A graph cut
algorithm with Gaussian mixture models (GMMs) for lung segmentation was
proposed to improve the lung nodule detection [113].
A neural network based unsupervised learning algorithm was proposed in [86], which
used dot enhancement filter to select nodule candidates by enhancing spherical-
shaped objects. The neural classifier was developed, which used a training elimination
approach similar to the Self-Organization Map (SOM) for false positive reduction.
2.2.3 Diagnosis Based on Template Matching
Template matching is an emerging area in computer vision. It is an effective way to
recognize the objects from an image by using a template image [139]. In medical
imaging use of template matching has gained a profound interest in recognizing
abnormal growth within the human organs [140]. However, there is one limitation that
it cannot produce good results with small sized objects. Lee, et al. [75] developed two
template matching techniques. The first technique, named Genetic Algorithm
Template Matching (GATM) was used for extracting nodules. The second was a Lung
Wall Template Matching (LWTM) technique supported by conventional semicircular
models to detect those nodules which were attached to the lung walls. After initial
identification, the next task was to improve the results by reducing the false positive
rate for which they used thirteen features containing inverse difference moment,
entropy, area, and contrast. GATM was extended using the shape properties of
nodules [49]. 3D shape based features combined with global intensity distribution
were used to define fitness function.
27
Osman, et al. [85] presented a 3D template matching technique for lung nodule
detection. At the first step, ROI was identified by an eight directional search in each
slice. Combining 2D images, a 3D image was generated, which was matched with a
predefined 3D template to identify nodules. The evaluation of this technique was
performed only over six cases of LIDC dataset. Ozekes, et al. [51] proposed Genetic
Cellular Neural Networks (G-CNN) for region lung segmentation. The use of Fuzzy
rule based thresholding produced 100% sensitivity but a high false positive rate.
2.2.4 Diagnosis Based on Fuzzy Logic
Fuzzy logic is used in various kinds of decision problems [141]. It is based on
approximate reasoning ranging from a decision value from 0 to 1. It has some
applications generally in image processing and particularly in medical imaging. Lin
and Yan [78] proposed a fuzzy logic based pulmonary nodule detection method. In
the first step, lung area was extracted using some image processing techniques
comprising thresholding, morphology closing, and labeling. From the segmented lung
region, three features circularity, area, and mean brightness were extracted. A fuzzy
neural model was created and trained by these features to detect pulmonary nodules
with good accuracy. Lin, et al. [82] proposed another fuzzy-based method, which was
an extension of the previous approach with improvement in false positive rate. A
fuzzy rule based system using 2D and 3D features was given in [102]. The system
was optimized using a genetic algorithm (GA).
2.2.5 Diagnosis Based on Thresholding
Thresholding is a simplest but an effective way to identify objects within the image
[142]. It is also a very useful method for image segmentation by using pixel
intensities to partition the image [143]. In medical imaging, thresholding is a very
useful method to identify objects like nodules, vessels, and lesions depending upon
different gray levels. A thresholding based diagnosis system for cancer detection from
chest CT images was presented in [60]. Multiple thresholding operations were
performed during segmentation, edge enhancement, background elimination, and
nodule boundary detection. Finally, a probabilistic neural network was applied by
shape and texture features to distinguish the infected and normal sections of the
image. Thresholding also produced good results in detection [74]. To extract lung
28
volume from image gray-level thresholding was used as a segmentation technique.
After that, three-dimensional objects were extracted as nodule candidates, and finally,
they used linear discriminate analysis for final nodule identification with better
performance. Mukhopadhyay [58] proposed a segmentation framework for lung
nodules. The author used different thresholding techniques to segment region of
interest (ROI) and nodules of different types. The method in [59], performed 2D
preprocessing, 3D analysis and finally feature extraction. In 2D preprocessing, lung
volume was extracted using gray level thresholding. In the next step, thirty-six
thresholds (ranging from 50 to 225 gray levels) were applied to identify the nodule
candidates. Finally, to reduce false positive rate, a feature vector with nine features
was created, including three gray levels, three morphological and three 2D features.
On the basis, of this feature vector classification of nodule and non-nodule was
performed by using linear discriminative analysis.
A local density maximum algorithm was proposed by Zhao [79]. The diagnosis
process mainly consisted of three steps in which different types of thresholds were
used to segment lung portion, nodule candidate selection, and false positive reduction.
Shape and texture feature based juxta-pleural nodule detection method was proposed
by Taşcı and Uğur [108], in which they introduced seven new features. Otsu
thresholding and morphological operations were used for segmentation, and finally,
for classification, they tested ten different classifiers and found the best results for the
generalized linear model regression (GLMR) classifier. Teramoto and Fujita [97]
proposed a nodule enhancement method to improve the detection process. The
thresholding was applied for ROI extraction, and a cylindrical filter was proposed to
improve the detection process.
2.2.6 Diagnosis Based on Mathematical Morphology
Mathematical morphology is an effective image processing technique used in
segmentation [144]. Various researchers have developed pulmonary nodule detection
methods based on the morphological operations fully or with partial support. Kostis,
et al. [80] performed an automated diagnosis based on mathematical morphology.
This technique used a different combination of morphological filters to extract lung
nodules from high-resolution CT images. A morphological matching algorithm
improved the detection performace [81]. The system comprised of a combination of
29
2D and 3D of morphology operations. At first, 2D operations were performed based
on gray-level thresholding combined with morphological operations to extract lungs
portion from CT image. After that, 3D region growing and labeling with the support
of morphological closing were used, which further leads to extract the nodule
portions. Finally, by shape and nodule geometry classification was performed to
differentiate nodule and non-nodules.
Zhang, et al. [123] applied various morphological operations combined with other
image processing and machine learning techniques. At first, lung region extraction
was performed by the active counter model. After lung region segmentation, nodule
candidates were detected by using thresholding, morphological operations, and
connected component labeling. After that, feature extraction was performed
comprising of 3D skeletonization features, texture and shape features.
2.2.7 Multiple Techniques/Hybrid Approaches
Recently, researchers are interested in developing hybrid techniques by combining
multiple algorithms. Ye, et al. [57] presented a multistep method which comprises
various algorithms to detect lung nodules automatically. In the first step, segmentation
was performed on input CT images to separate the lung portion from the remaining
parts of the image. Fuzzy thresholding algorithm was used for this purpose. In the
next step, preprocessing was performed on the segmented regions; for this purpose
anti-geometric diffusion was used. In this method, the edges were diffused from the
image which supports the precise geometric feature extraction. A weighted SVM
minimized the chances of FPs resulting in more than 90% accuracy. Another lung
nodule detection algorithm comprising of six stages was presented in [88]. At the
first stage, they eliminated the additional information related to the patient‘s body and
labeled the stage as thorax extraction. In the next stage, they extracted the correct
portion of lung parenchyma [145] to support the accurate identification of dense
structures inside it. Afterwards, the tubular structure was eliminated. All these
eliminations improved the identification of correct portion of nodules. These stages
used thresholding, region growing, genetic algorithm and SVM [105]. The technique
was tested against LIDC-IDRI dataset with 80%, 20% data for training and testing,
respectively.
30
A multilevel segmentation technique for lung nodule detection presented in [106].
The fuzzy connectedness method comprised of mask generation, automatic seed point
selection, and adaptive thresholding. The process was used to remove the blood
vessels. In the next phase, automatic seed point selection was performed, which was
based on thresholding and 2/3D connectivity analysis. Finally, genetic algorithm and
multiple morphological operations were performed for final detection. For
experimentation images from LIDC and LIDC-IDRI databases were used and 100%
detection rate with 5% false positive was achieved.
Brown, et al. [107] presented a segmentation and detection method of nodules based
on watershed, intensity thresholding and Euclidean Distance Transformation (EDT).
Han, et al. [61] proposed vector quantization (VQ) method for lung ROI segmentation
and nodule candidate detection. A hybrid method was developed that included various
techniques applied to CT images one after other [56]. The system diagnosed cancer in
three major steps including lung tissue segmentation based preprocessing step, a
filtering step, and false positive reduction step. The histogram thresholding, seeded
region growing, and morphological filters were applied to the input CT images for
preprocessing and extraction of lung volume from the raw images. The nodule
candidates were identified, and geometric features were extracted using a 3D radial
based filter and scale space analysis, respectively. The final step was the false positive
reduction, in which two approaches were used, including heuristic and supervised. In
the heuristic approach, simple thresholding based on geometric features was used to
reduce the number of ROIs. On the other hand, in a supervised approach SVM was
used based on heuristics and maximum intensity projection Zernike moments [146].
A hybrid method was proposed by Shen, et al. [115] to identify juxta-pleural nodules
from CT images by combining chain code and SVM classifier. Bidirectional chain
code was used to detect nodule boundary. Three features, including relative boundary
distance, boundary segment concave degree, and relative position information were
used to train the classifier. Finally, SVM was used for classification. Jaffar, et al.
[124] used differential evolution based thresholding for lung segmentation, and then
ensemble learning was applied on the basis of gradients and intensity features for
classification of nodules.
Teramoto and Fujita [125] proposed a hybrid method for PET/CT images.
Thresholding and cylindrical nodules enhancement filters were applied. Rule based
31
classifier and three SVM classifiers were trained over shape features for final
classification of nodules. Gong, et al. [126] presented a hybrid diagnosis method. Otsu
thresholding, region growing and morphological filtering were applied for lung region
extraction. Afterwards, 3D tensor filtering and level set segmentation along with local
image information was used for nodule candidate detection. Subsequently, shape,
intensity and texture features were extracted. On these features, random forest
classifier was trained and tested to distinguish nodules and non-nodules.
2.2.8 Deep Learning Based Methods
Artificial intelligence technology is changing the lives of people and becoming a hot
spot in the development and research of science and technology [147]. Deep learning,
such as artificial intelligence technology, builds a neural network imitating the
neurons of a person to form the learning machine is a unique idea in the field of
artificial intelligence. In particular, in recent years, deep learning has become an
important and dynamic area of research that provides a platform for different
classification problems [148]. The key feature of deep learning is to create an
extensive network of neurons that improves the accuracy of classification and
prediction problems [149]. A major focus of deep learning research in computer
vision is on natural images. However, in recent years, the researchers have also
applied deep learning algorithms in medical imaging, but their results are not
promising [118].
Hua, et al. [116] used a deep belief network for lung nodule classification using
texture and morphological features and compared the performance with the
conventional neural network. Another deep learning method was presented by Sun, et
al. [121]. They used three deep learning algorithms, including autoencoder, deep
belief network and CNN. The method was evaluated over LIDC-IDRI dataset. Dou, et
al. [119] also proposed deep learning based method. A multi-level 3D CNN
framework was developed for this purpose. Another deep learning based method was
proposed by Ali, et al. [127]. Reinforcement learning based deep neural network was
trained and tested for automated detection of nodules. da Silva, et al. [120] proposed
nodule classification method by combining deep learning and genetic algorithms.
Jiang, et al. [128] presented a patch based deep learning approach for lung nodule
detection from 2D images. The lung segmentation was performed based on connected
32
component analysis and morphological operation. The multiscale Frangi filter was
used for nodule candidate detection by removing the vessels. Finally, classification
was performed based on CNN. The method was tested over LIDC-IDRI dataset.
Li, et al. [129] designed three convolutional neural networks with three different input
sizes, including 12x12, 32x32 and 60x60, for nodule candidate detection. For
classification of nodules, they developed an ensemble CNN and achieved 95%
sensitivity with 5.0 FPs per image. Xie, et al. [132] proposed 2D CNN for nodule
candidate detection and boosting technique. Kasinathan, et al. [133] enhanced a CNN
based on AlexNet architecture for nodule classification. The candidate nodules were
detected based on ACM and local image bias field formulation. The method achieved
97% sensitivity with 3.8 FPs/image.
2.3 Discussion and Analysis
The current review of lung nodule detection methods mainly focuses on the methods
given during recent times. These methods are critically viewed under some analysis
parameters, especially on performance, nodule size, datasets, and nodule types. The
primary focus of a diagnosis system is the detection and classification of nodules with
high sensitivity and less number of false positives. It is a complicated task to compare
the soundness of different methodologies due to the different parameters used during
their analysis, e.g. if two diagnosis systems have produced the results on different
datasets, focused different kinds of nodules as well as sizes then it is quite
unjustifiable to compare those. In recent years, some authors have claimed sensitivity
higher than 95% [51, 54, 84, 85, 90, 109]. However, these methods were unable to
overcome the false positive rate. Osman, et al. [85] and Ozekes, et al. [51] achieved
100% sensitivity, but their results are on a dataset containing only large nodules of
size greater than 5mm. Lung nodule detection methods lack in achieving high
accuracy and have high false positive rate due to limitations in the handling of
different types of nodules like juxta-pleural and juxta-vascular nodules, especially of
smaller sizes due to the following reason. These issues are due to the complex
structure of the human lung.The limitations of the existing methods are illustrated in
Table 2.2.
33
Table 2.2: Limitations in recently developed methods
Methodology Year Limitations
Ozekes, et al. [51] 2008 Used larger nodules of >5.6 mm. High FPs/scan of 13, template
matching was used and failed to deal with smaller nodules.
da Silva Sousa, et al.
[88]
2010 Low sensitivity of <85%. The method was not evaluated over some
standard datasets. Large size nodules were included in the evaluation.
Messay, et al. [89] 2010 Accuracy is just 80%; juxta-pleural nodules were missed during the
smoothing process.
Magalhães Barros
Netto, et al. [93]
2012 Sensitivity is low at 85.9%; evaluation was performed with a small
dataset containing 48 nodules.
Keshani, et al. [101] 2013 Low sensitivity 89% and high FPs/scan 7.3, multiple diseases were
handled with features of the same type resulting in poor performance.
Choi and Choi [98] 2013 Used a subset of dataset for performance evaluation.
Choi and Choi [54] 2014 High false positive rate of 6.67/scan; evaluation was performed over
subset of dataset containing 148 nodules.
Kuruvilla and
Gunavathi [103]
2014 Only statistical features were used. A very high false positive rate of
30/scan.
Cao, et al. [104] 2014 Accuracy < 85%. False positives were not identified.
de Carvalho Filho, et
al. [105]
2014 Sensitivity is very low 86%. FPs/scan not identified.
Badura and Pietka
[106]
2014 Accuracy is given, but no values of sensitivity and specificity are
mentioned. It used only those nodules which were annotated by all
radiologists.
Wang, et al. [110] 2015 Performance is not properly measured; only false positive rate is
mentioned. Performance of system not evaluated by a standard dataset.
Shen, et al. [115] 2015 Method is evaluated only in terms of accuracy. It handled juxta-pleural
nodules only.
Kaya and Can [111] 2015 Low sensitivity of 82%. Number of FPs not reported.
Hua, et al. [116] 2015 Poor results of 73% sensitivity, 82% specificity. No. FPs mentioned.
Features are not properly elaborated. Method is not tested over some
standard dataset.
Taşcı and Uğur
[108]
2015 Method was evaluated in terms of accuracy. Only juxta-pleural
nodules were addressed. Only one type of feature was used. FP not
reported.
Mukhopadhyay [58] 2016 Very low sensitivity. Only segmentation is performed.
34
Methodology Year Limitations
Firmino, et al. [117] 2016 Only one type of feature was used for classification. HOG features
Nibali, et al. [118] 2017 A large number of deep features are fused which increases the
computation time. Moreover, FPs are not reported.
Zhang, et al. [123] 2018 Used a smaller subset of LIDC-IDRI dataset of 71 scans containing
168 nodules.
Huidrom, et al.
[130]
2019 The performance was evaluated over 300 scans, but nodule
information is not provided. Moreover, the number of FP was not
reported.
Shaukat, et al. [131] 2019 Simple thresholding was applied to extract the lung ROI. It leads to
missing the juxta-pleural nodules. The method was evaluated on 84
scans.
Xie, et al. [132] 2019 The nodule detection is based on 2D properties, which leads to the
wrong diagnosis. As a result, sensitivity is low as 83% and FPs are
very high as 8/scan.
Following are the major findings of this review:
The diagnosis methods miss juxta-vascular nodules during the process of
vessel removal, which leads to the wrong diagnosis [35, 48, 54-56, 89, 94, 97,
103, 124].
There is a challenge of high sensitivity with low FPs/scan [48, 54-56, 61, 89,
94, 97, 103, 107, 118, 119, 123, 124, 127].
One type of feature vector cannot represent all kinds of nodules [83].
In morphological operation, window selection cannot work well for all slices
and can miss juxta-vascular nodules [84].
The segmentation process can completely or partially eliminate juxta-pleural
nodules [55]. During the diagnosis process, a fundamental step is the
extraction of the lung portion. Different segmentation methods are used for
this purpose. During this process, juxta-pleural nodules are completely or
partially removed from ROI due to their presence on the boundary of the lung.
The detection of these nodules is another challenge.
Template matching leads to wrong candidate selection due to similar shaped
objects, especially for small nodules [49, 51, 75, 85].
35
The nodule candidate detection is a key step in intelligent lung nodule
detection and classification.
The shape features, along with intensity and texture features can be better
representative of the nodules.
Considering these findings, we have designed our proposed methods to improve the
detection process and reduce the number of false positives. In the first phase of lung
region extraction, optimal thresholding is applied which varies from slice to slice. It
results in precise segmentation. Further analysis is also performed for boundary
correction to ensure the detection of juxta-pleural nodules. In the initial candidate
detection process, 3D properties are taken into account. In addition, 2D as well as 3D
connectivity and shape properties of nodules and vessels are incorporated to separate
the nodules from vessels. In the third phase of feature extraction, instead of using one
type of feature, shape, intensity, gradient and texture features are extracted.
Furthermore, both 2D and 3D features are extracted for better identification of
nodules. In Method II and IV, feature reductions are also applied which reduces the
training time of the classifier. In the final phase of classification, we investigate the
effects of single classifier (Method I), multiple classifiers (Method II), ensemble of
classifiers (Method III) and deep learning (Method IV). This impacts performance and
also helps in comparative analysis. Furthermore, in Method III and IV we have
applied 3D segmentation of juxta-vascular nodules instead of conventional 2D
segmentation, which requires restructuring of nodules. This improves the overall
performance in general and the detection of juxta-vascular nodules in particular.
Another key feature of the proposed methods is that these have the ability to deal with
different types of nodules, including isolated, juxta-pleural or juxta-vascular.
2.4 Summary
In this chapter, a systematic review has been presented of diagnostic systems for
pulmonary nodules from CT images. Our analysis found that LIDC-IDRI is the
largest and the most authentic benchmark for performance evaluation of diagnostic
methods, compared to other databases. Additionally, feature extraction, classification,
and segmentation techniques were discussed in detail related to different diagnosis
systems. During the analysis of performance evaluation of diagnosis systems, an issue
of high false positive rate produced during the nodule detection process was primarily
36
due to juxta-pleural, juxta-vascular and smaller nodules. Consequently, there is a need
to overcome these issues to improve the performance of diagnosis systems. This
review will help researchers to find accurate information about lung cancer and its
diagnosis.
37
Chapter 3
Proposed Methods
38
3.1 Introduction
This chapter presents four methods for lung nodule detection and classification, which
are proved to be more effective than the existing ones. In the first method, optimal
thresholding and polygon approximation is performed for ROI extraction and nodule
candidate detection respectively. Nodule candidates are enhanced, and a hybrid
feature vector is proposed by encapsulating shape, intensity, and HOG features. SVM
is applied for the classification on the basis of the proposed hybrid feature vector. In
the second method, nodule candidate detection is improved by applying ACM and
shape properties of nodules. Moreover, feature reduction is performed to create a
better feature descriptor, which improves the classification performance. Four
classifiers are trained and tested to classify the nodules. In the third method, a novel
3D multistage segmentation method is proposed to improve the lung volume
segmentation and nodule candidate detection process. Furthermore, SVM-ensemble
classifier is proposed for better classification of nodules. In the fourth and final
method, a diagnosis method is proposed in which every step of the diagnosis process
is improved. Threshold selection is improved by FODPSO optimization. A novel
method for nodule candidate detection is proposed based on geometric fit in
parametric form. In feature extraction, 2D, as well as 3D shape and texture features
are extracted. In texture features, spatial information is also incorporated to extract
true representative features. Finally, deep learning approaches, including autoencoder
and softmax are trained and tested for feature reduction and classification. A block
diagram in Fig 3.1 illustrates all the proposed methods and their major steps. The
proposed methods are listed below, and details are discussed in the succeeding section
of this chapter.
i. Lung nodule detection using polygon approximation and hybrid features from
lung CT images
ii. 3D nodule candidate detection supported by hybrid features to reduce false
positives
iii. Multistage segmentation model and SVM-ensemble for precise lung nodule
detection
iv. Lung nodule detection and classification based on geometric fit in parametric
form and deep learning
39
Fig 3.1: Block diagram of proposed methods
40
3.2 Method I: Lung Nodule Detection using Polygon Approximation
and Hybrid Features from Lung CT Images
A lung nodule detection method is presented in this section which is based on CT
images.
Fig 3.2: Major steps of the proposed method
The pictorial representation of basic units of the proposed system is given in Fig 3.2.
It shows three significant steps, which are further divided into substeps. At first, lung
region is separated by optimal gray level thresholding and morphological operations.
On the next step, polygon approximation is used to extract nodule candidates from the
region of interest (ROI) by eliminating vessels. After that, nodule candidates are
enhanced, which leads to improved classification performance. Finally, the false
positive reduction step is performed, comprising of two substeps, feature extraction
and classification. A hybrid feature vector is provided as input to the classifier which
41
is composed of geometric, intensity and Histograms of Oriented Gradients (HOG)
features. And for classification, SVM is used to distinguish nodules and non-nodules
by features, extracted in the previous step.
The main contributions of this study are:
1. Lung region extraction is performed on the basis of optimal gray level
thresholding.
2. A novel nodule candidate detection method based on polygon approximation
is proposed.
3. A hybrid feature vector is created for better training of classifiers.
4. SVM classifier is trained and tested with different kernels for better
classification of nodules.
3.2.1 Lung Region Extraction using Optimal Threshold
Thresholding is a simple and effective method of segmentation in image processing
[142]. Segmenting a grayscale image creates a binary image with reference to the gray
values of related regions. If the intensity of a pixel is less than the threshold, then the
corresponding pixel in the mask image is set to white, and otherwise black. However,
in some cases, a single threshold may not be effective. Similar situation occurs in the
lung region extraction process, since in a 3D scan, middle slices may require different
threshold as compared to the starting and ending slices, because the middle slices
contain more details of the lung. So, there is a need to update the threshold or use of
multiple thresholds. The pixel intensities of the lung region in a CT image are mostly
in the range of -950 to -500 Hounsfield Unit (HU). The extent of dense area,
including bone, blood, and the chest wall, is higher than -500 HU [150]. The air
attenuation is at -1000 HU. Taking into account these points, -500HU is selected as an
initial threshold . This threshold is updated using an iterative process based on the
Eq. 3.1.
(3.1)
where is the threshold of the iteration, is the mean intensity value of the
foreground which is the lung region, and is mean intensity value of the
background, segmented in iteration on the basis of the threshold . This
42
process is repeated until an optimal threshold is computed as shown in Fig 3.3. If a
pixel at position is greater than threshold then the corresponding pixel in
binary image at the same location will be 1 otherwise 0.
Fig 3.3: Lung region extraction process
The ROI is extracted using the lung mask, which includes both vessels and nodules
that are further processed to remove vessels. Figure 3.4 shows input images, their
corresponding histogram and binary mask generated after applying the threshold.
(a) (b) (c)
Fig 3.4: Gray level distribution (a) input CT images (b) histogram of images (c)
threshold images
43
3.2.2 Nodule Candidate Selection using Polygon Approximation
One of the critical steps in lung nodule detection is candidate nodule extraction and
elimination of unwanted shapes in ROI. A more precise selection leads to better
performance of a diagnosis system. A polygon approximation method is proposed to
distinguish nodules from vessels. In this technique, a ratio between the length and
height of polygon surrounding the shape within ROI is calculated. If this ratio is more
than the threshold specified, then the shape is eliminated. Otherwise, it is selected as a
nodule candidate. Fig 3.5 illustrates the candidate selection.
(a) (b)
Fig 3.5: Nodule candidate selection (a) objects in ROI (b) marked by the polygon
In polygon approximation, the objects are distinguished by the ratio of polygon sides
as given in Eq. 3.2.
(3.2)
where represents the width and indicates the length of the polygon is enclosing
the candidate object. Algorithm 3.1 is proposed to detect the nodule candidates using
this ratio formula. The structure of vessels is rectangular whereas the nodules are
round shape objects. Therefore, the objects with a ratio closer to 1 have a high
probability of being an actual nodule, whereas objects with smaller ratio are more
likely to be a vessel. In Algorithm 3.1, the value of ratio threshold is set to 0.5, which
depicts that the objects, with a width less than half of their length, are considered as
vessels and vice versa.
44
Algorithm 3.1: Nodule Candidate Refinement
Input: Set of segmented candidate object:
Output: Refined nodule candidate set:
Begin
1: ► Set of nodule candidates
2: ► Set of vessels
3: for each ► Process each segmented object in
4: if ( ) then ► Ratio computed by Eq. 3.2
5: ► Add the object to set of vessels
6: else
7: ► Add the object to set of nodule candidates
8: end if
9: end for
End
3.2.3 Nodule Candidate Enhancement
Contrast enhancement plays a critical role in image segmentation and classification to
achieve better performance by improving the image quality. Herein, the images
containing nodule candidates are enhanced using Contrast Limited Adaptive
Histogram Equalization (CLAHE) [151]. In this approach, rather than using global
contrast enhancement, contextual contrasts are enhanced. This technique distributes
gray values using a full range of gray to improve the hidden features of the image. To
reduce the impact of noise, contrast limiting is applied to neighborhood pixels.
CLAHE is an enhanced version of Adaptive Histogram Equalization (AHE) [152].
The process of candidate nodule enhancement improves the quality of objects within
the image, thus helping in distinguishing nodules and non-nodules, and improving the
overall performance of the diagnosis method.
3.2.4 False Positive Reduction using Nodule Properties
In this step, the initial nodule candidate set is refined on the basis of nodule shape
properties. In LIDC, the size of nodule has a range of 3mm to 30mm. These objects
are removed using the above criteria to improve the set of nodule candidates, which
45
helps in reducing false positive rate. Figure 3.6 is the pictographic representation of
the process.
(a) (b)
(c) (d)
Fig 3.6: ROI and nodule segmentation (a) input CT image (b) lung mask (c) ROI with
vessels and nodules (d) nodule candidate region
3.2.5 Hybrid Intensity-Geometric and Gradient Feature Extraction
After candidate nodules detection, the next step is to detect actual nodules and non-
nodules. For this purpose, there is a need to classify nodule candidates by their
features. In the proposed method, a hybrid feature vector is created, which comprises
of geometric, intensity and HOG features, described in succeeding sections.
3.2.5.1 First Order Histogram Based Intensity Features
The intensity characteristics play a vital role in object detection. First order histogram
is used to extract intensity features that quantitatively describe statistical
characteristics of an image [153]. The features obtained in this process present a
useful characteristic of the image. One of the intensity features is a mean value, which
measures an average value of image intensity. Similarly, variance, skewness, and
kurtosis are also used to evaluate intensity distribution within the image. Skewness is
a measure that tells the symmetries of the image. Kurtosis is the flatness of the
histogram. Energy is a measurement of local homogeneity in the image that represents
46
uniformity of texture. Histogram entropy measures the randomness of the image. The
mathematical representations of these features are given from Eq. 3.3 to Eq. 3.8.
∑
(3.3)
∑
(3.4)
∑
(3.5)
∑
(3.6)
∑
(3.7)
∑
(3.8)
where is maximum gray level in the image and is the probability of a gray level
computed by the first order histogram.
3.2.5.2 Geometric Features
The geometric features are useful to describe the shape of an object within the image
[154]. In addition to intensity features, the proposed technique makes use of
geometric features, which are further subdivided into 2D and 3D features. 2D features
are based on the information extracted from a slice. Whereas, 3D features are based
on 3D scans by examining voxals. It describes the shape of objects in the nodule
candidate set. The area as a geometric feature is given by Eq. 3.9 below.
Area ∑ (3.9)
where is a median slice of the 3D scan; the median slices have more details of
internal structure as compared to other slices. Eq. 3.10 gives the mathematical
representation of the radius feature.
47
(3.10)
where D is diameter which represents the maximum length of the bounding box. Eq.
3.11 gives the 3D volume feature.
∑
(3.11)
where V represents the total number of voxels in the segmented object, circularity and
compactness are presented by Eq.3.12 and Eq.3.13 respectively.
(3.12)
(3.13)
where A and r are the area and radius respectively as defined in Eq. 3.9 and Eq. 3.10.
These are useful features for image analysis to recognize the geometric shape of
objects. In the current scenario, these features are intended to play a vital role to
distinguish nodules and non-nodules based on their geometrical structure.
3.2.5.3 Histogram of Oriented Gradients
In this method, gradient orientation in a localized portion of an image is counted
which is similar to the process of the histogram of orientation contrast of edges. It is
useful to recognize the shape of an object by computing gradient orientation in a local
region, containing the object [155]. Figure 3.7 shows nodule candidates and HOG
descriptor.
(a) (b)
Fig 3.7: Gradient orientation (a) nodule candidates (b) HOG descriptor
48
3.2.6 Support Vector Machine for Classification
After the formulation of feature vectors, SVM is used to classify the nodules. SVM is
the leading classifier in the domain of statistical learning [68, 156]. The reason behind
the use of SVM is its strength of handling high-dimensional data by using kernel
tricks. Secondly, SVM avoids overfitting by the use of regularization parameter, and
finally, it avoids local minima and is more robust in convergence to global minima.
Let { } are the examples on whom SVM has to perform
classification. The optimization function is given in Eq. 3.14 and Eq.3.15.
∑
(3.14)
(3.15)
where is weight vector and represents bias. The kernels are represented using Eq.
3.16 to Eq. 3.18.
Linear Kernel (3.16)
Polynomial Kernel
(3.17)
Radial Based Function ( ‖ ‖
) (3.18)
where and are vectors in input space, represents the degree of polynomial,
‖ ‖ is the Euclidian distance and is standard deviation, defined as Gaussian
distribution.
The proposed method is evaluated over a publicly available dataset LIDC. The results
are discussed in the next chapter.
3.3 Method II: 3D Nodule Candidate Detection Supported by
Hybrid Features to Reduce False Positives
The nodule candidate detection is a key step in intelligent lung nodule detection and
classification. In the previous method, the nodule candidate detection is performed
49
from 2D scans. Moreover, candidates are refined on the basis of the ratio of major and
minor axis lengths. In this study, the nodule candidate detection process is improved
by considering the 3D properties of nodules, in addition to 2D properties.
Furthermore, some additional parameters along with ratio are also used for better
refinement of nodule candidate set.
The proposed method performs four major tasks, as illustrated in Fig 3.8. It is
necessary to remove the background before the start of the nodule candidate detection
process. If nodule candidate detection is performed without removing the background,
then there is a chance that many objects residing outside the lung region may be
included within the nodule candidate set, which may lead to the wrong diagnosis. To
avoid this issue, optimal gray level thresholding, connected component labeling and
hole filling for the lung region extraction is applied. In the second step, a novel
method for nodule candidate detection is proposed based on Active Contour Model
(ACM) [157], 3D neighborhood connectivity and geometric properties based rules. In
this phase, some of the false positives are eliminated by checking the 3D
neighborhood connectivity and by validating the geometric properties of the candidate
objects. In the third phase, seven feature vectors are created with individual and
combinations of geometric, texture and HOG-PCA features. Finally, to reduce false
positives, classification is performed. Four different classifiers including SVM, k-NN,
Naïve Bayes and AdaBoost are used with different variations.
The major focus of this study is the reduction of false positive nodules, along with a
good score of sensitivity. The main contributions of this study are as under:
1. Lung region extraction is performed on the basis of optimal gray level
threshold based on HU values.
2. A novel hybrid technique for nodule candidate detection is proposed by
encapsulating ACM, 3D connectivity and geometric properties of nodules.
The method selects optimum candidate set, which reduces the computation
time of proceeding phases of the diagnosis process.
3. A hybrid feature vector is created for better representation of nodule
candidates. The size of each individual feature vector is also considered as a
major factor. A smaller vector may lose its significance if it is encapsulated
with a very large feature vector. To overcome this issue HOG features are
50
reduced first to make compatible in size with the other two base feature
vectors, including texture and geometric feature vectors.
4. Finally, four different classifiers including SVM, k-NN, NB, and AdaBoost
are trained and tested to distinguish actual nodules from the candidate set.
These contributions have impacted the performance of the diagnosis method.
Fig 3.8: Major steps of the proposed method
3.3.1 Lung Volume Extraction using Optimal Threshold
The initial lung mask is extracted using the optimal threshold. After that 3D
connected component labeling, morphological dilation and opening operations are
used for hole filling and boundary smoothing. To include the nodules attached to the
lung‘s boundary, also known as juxta-pleural nodules, the bidirectional chain code
[115] is applied, which results in boundary correction of the segmented mask. The
lung region is separated by applying the lung mask over the corresponding input slice,
which results in the segmented image with extracted ROI. The resultant ROI contains
the lung portion of the CT image.
51
(a) (b) (c)
Fig 3.9: Lung region extraction (a) input CT images (b) histogram of images (c) final
lung masks of the images
Fig 3.9 shows that different slices have different intensity distributions, due to which,
a single threshold cannot work for every slice. The images in Fig 3.9(a) represent the
input CT images. These images are chosen to show the different views of a scan. The
slices shown in Fig 3.9(a) are slice-number 14, 96, 122 and 142 of LIDC scan
13614193285030092. The thresholds are calculated based on the HU intensity values
52
in the images. The histograms are shown in Fig 3.9(b) illustrate the HU distribution
within each image.
3.3.2 Nodule Candidate Detection
In a diagnosis system, the candidate nodule extraction and elimination of unwanted
shapes is an important step. A more precise selection leads to better performance of a
diagnosis system. The inclusion of unwanted objects in the candidate set affects the
performance of the detection method. However, strict criteria may lead to missing the
actual nodules, resulting in the wrong diagnosis. Considering these issues, a 3D
nodule candidate detection method based on the Active Counter Model (ACM), 3D
neighborhood detection and geometric properties of nodules is proposed. Figure 3.10
illustrates the working model of the proposed candidate detection method.
Fig 3.10: Nodule candidate detection process
The ACM [157], also known as a snake, is an effective method for image
segmentation. The major advantage of ACM is that it extracts the objects from an
image with closed and smooth boundaries. The fundamental idea of ACM is to extract
the desired object from an image by energy minimization. It comprises of three types
of energies including internal energy, external energy, and constraint energy. The
relationship of a snake with these energies is shown in Eq. 3.19.
(3.19)
53
Moreover, the internal energy is comprised of elastic energy and bending energy
given from Eq. 3.20 to Eq. 3.23. This elastic energy is given by the first-order
derivative weighted by (s). Scalar controls the contribution of elastic energy using
point spacing. The bending energy contribution is controlled by the product of second
order derivate and scalar . The scalars and control the snake to identify the
object boundary, resulting in precise segmentation.
∫
(3.20)
⁄ (3.21)
where represents the snake position and is elastic energy controlling weight
along the contour.
∫
(3.22)
⁄ (3.23)
External energy takes smaller values as the boundaries of ROI and is defined as a
function of Eimage(x, y) shown in Eq. 3.24 and Eq. 3.25.
∫ ( )
(3.24)
( ) (3.25)
where is Gaussian of standard deviation , and represents the snake position.
represents the gradient operator, and represents the linear convolution. These
energies generate a flexible cover that surrounds the object and useful for its
segmentation. This technique is applied to segmented ROI containing nodules and
vessels. ACM results in detection of boundaries of the objects within ROI, which
includes vessels and nodules. It creates a large number of objects within ROI, which
can become the candidates for nodule. In addition, ACM also eliminates very small
54
objects which help in reduction of noise objects. These candidate objects are refined
on the basis of 3D connectivity and geometric properties. Neighborhood detection is
important as nodule is a mass that must have its existence in multiple adjacent slices
within a CT scan. If an object has existence in one slice, then it must have similar
intensity values in the predecessor or successor slices. Using this property, unwanted
objects are removed from the ROI. In the next step, the nodule candidate object‘s
filtration criteria are defined, by using geometric properties of the nodule. Fig 3.11(a)
shows ROI extracted from input CT images. The individual objects are extracted from
ROI using active contour shown in Fig 3.11 (b). These objects are filtered through 3D
neighborhood detection. After that, nodule candidates are extracted using Algorithm
3.2.
Algorithm 3.2: Refining Nodule Candidates
Input: Set of segmented candidate object:
Output: Refined nodule candidate set:
Begin
1: ► Set of nodule candidates
2: ► Set of vessels
3: for each ► Process each segmented object in
4: if ( ) then ► Ratio computed by Eq. 3.2
5: ► Add the object to set of vessels
6: else if ( and and
7: ► Add the object to set of nodule candidates
8: else
9: discard ► Other than vessel or nodule
10: end if
11: end if
12: end for
End
55
(a) (b) (c)
Fig 3.11: Nodule candidate detection (a) segmented ROIs, (b) corresponding contours of
the objects within the ROI’s, (c) the final selected candidates.
In Algorithm 3.2, nodule geometric properties are used, including the ratio of length
and breadth, area and circularity, to refine the nodule candidate set. The mismatched
objects are removed from the candidate set, which helps in reducing false positive
rate. The input to Algorithm 3.2 is segmented images containing lung ROI. First, the
automated contour is applied, to extract all the objects in the ROI. To extract nodule
candidates, the vessels and noise objects are eliminated from the set of segmented
objects. The ratio between the length and width of each object is computed by using
the bounding box over each object. A set is created that includes segmented objects
from ROI. Two additional sets named and are also created. Initially, and
sets are empty. Then between the length ( ) and width ( ) of each object is
computed and compared with , where stands for ratio threshold. If is
smaller than the threshold , it is considered as a vessel and not the round shaped
object. If the selected object is not a vessel, then it is checked with respect to the other
nodule properties i.e., area and circularity. Two thresholds for area and
are
defined, where stands for the lower threshold and
stands for the higher
56
threshold of the area. Furthermore, is defined as circularity threshold. If the
condition at line number six in Algorithm 3.2 is satisfied, then the object is considered
as nodule candidate; otherwise, it is considered as noise and rejected. Figure 3.11 (c)
shows the extracted nodule candidates from the corresponding images given in Fig
3.11 (a) respectively.
3.3.3 Feature Extraction
In the proposed technique, seven different feature vectors are created by different
combinations of geometric, texture and HOG-PCA features shown in Fig 3.12. The
texture, geometric and HOG features are used as base features. However, the HOG
features result in a large feature vector that is not compatible with the other two
features to become a part of the single vector. To overcome this issue, the PCA is
applied over the HOG features to reduce their dimensionality and make them
compatible with other features.
3.3.3.1 Texture Features
Texture features [158] are very effective in object identification and classification.
These features quantify the different properties of objects like smoothness, roughness,
bumpiness by using the intensity values of the object pixels. These features represent
the true characteristics of nodule texture and intensity in 2D, as well as 3D scan.
Taking into account these properties, these features are categorized into 2D texture
and 3D texture features. The 2D features include kurtosis, skewness, variance, mean,
entropy and energy. The 3D features include absolute value, correlation, inertia,
angular second moment, entropy, inverse difference, and maximum probability.
Angular second moment measures the uniformity of an object. Correlation measures
how much a voxel is associated with its neighboring pixels. Inertia measures the
contrast of intensity of a pixel with its neighborhood values within the candidate.
Absolute value computes the absolute intensity level in the candidate nodule. Inverse
difference measures the contrast of intensities within the object. Entropy measures the
randomness of the voxels in nodule candidates. If one combination of frequencies
dominates then maximum probability gives high value. The mathematical
representations of 3D texture features are given from Eq. 3.26 to Eq. 3.32.
57
∑ ∑[ ]
(3.26)
∑ ∑
(3.27)
∑ ∑
(3.28)
∑ ∑
(3.29)
∑ ∑
(3.30)
∑ ∑
(3.31)
(3.32)
where represents a maximum gray level intensity, represents the occurrence of
gray levels in the image. It gives the relationship between the intensities of pixels.
The are the standard deviations along horizontal and vertical axis respectively.
3.3.3.2 Geometric Features
The most important features, to distinguish a nodule from other objects, are the
geometric features because a nodule is a round shaped object with size 3-30 mm
[159]. In addition to this property, a nodule is a solid object which has its presence in
multiple slices in a CT image. Considering these properties of nodules, 2D as well as
3D geometric features are used. The 2D features describe the shape of an object
within a 2D slice of a 3D CT scan, whereas, 3D features represent the 3D shape
properties of an object within a 3D scan [57]. The 2D geometric features include area,
radius, perimeter, and circularity. The 3D geometric features include volume,
compactness, elongation, bounding box dimensions and principal axis lengths. The
bounding box dimensions give three features, corresponding to each dimension, i.e.,
58
x, y, and z, respectively. Principal axis length consists of two features including major
and minor axis lengths. As a result, eight 3D geometric features are extracted for each
candidate object. As a whole, twelve geometric features are included in the feature
vector by combining four 2D and eight 3D features.
3.3.3.3 Histogram Oriented of Gradient and Principal Component Analysis
(HOG-PCA)
Histogram Oriented of Gradient [155] features describe the directional change in the
intensities of the image. This property helps in object detection. Gradient distribution
provides useful information in distinguishing the nodules and non-nodules. HOG is an
important gradient descriptor. During the HOG feature extraction process, histogram
of the oriented gradient is calculated based on the pixel intensity, i.e., for each pixel
gradient magnitudes and , and orientation angle are calculated.
Where, are gradient magnitudes along the x and y-axis. These histograms are
combined to form a feature vector. To differentiate the nodules and non-nodules, edge
orientation intensities are used. Combining HOG features with shape and intensity
features provides valuable information of the nodule structures. However, a HOG
descriptor is a high dimensional feature vector which increases the computation time
of the classifier. To overcome this issue, PCA is applied to the HOG. PCA is a widely
used method for feature reduction [160]. It transforms the high dimensional data into
a vector by preserving the actual information. As a result, the top seven HOG-PCA
features are selected.
3.3.3.4 Hybrid Feature Vector
One type of feature vector cannot describe all the properties of an object. Considering
this issue, three types of features are encapsulated to create a hybrid feature. Let F =
(FG,FT,FH) is a hybrid feature vector, where FG, FT, FH are geometric, texture and
HOG-PCA features respectively, and are represented by Eq. 3.33 to Eq. 3.35.
59
Fig 3.12: Feature extraction and selection
⟨ ⟩ (3.33)
⟨ ⟩ (3.34)
⟨ ⟩ (3.35)
where , and are the lengths of individual feature vectors. In this way, feature
vectors of all the images are created, which results in very high dimensional data.
There is a need to select good representative features to overcome the high
dimensionality and improve performance. The researchers used PCA, LDA and
various kinds of optimization techniques for feature reduction. The common practice
is first to create a hybrid high dimensional feature vector and then select optimum
feature set. But there is a big issue that the ratio between a number of features for each
category mismatches. In this study, 12 geometric and 13 texture features are
extracted. However, the number of HOG features is very high as compared to
geometric and texture features. If a large number of HOG features are combined with
other features for feature selection, the resultant feature vector may not contain some
60
or all useful texture and shape features. Therefore, PCA is applied to the HOG feature
vector and seven top features are extracted. These features along with other two
feature vectors are created using geometric and texture features. Seven feature vectors
are created by different combinations of geometric, texture and HOG-PCA features.
Fig 3.12 shows the working model of feature extraction and selection process.
3.3.4 Classification
The final phase of the proposed method is classification, in which four classifiers are
used, including SVM [68], k-NN [161], Naïve Bayes [162] and AdaBoost [163].
These classifiers are trained and tested over the features extracted from nodule
candidates. SVM is a popular classifier used for two-dimensional as well as
multidimensional data by the use of kernel tricks [164]. To get the advantage of this
property, SVM is trained and tested with three kernels including linear, polynomial
and RBF.
The k-NN is used as the second classifier. It is a kind of lazy learner, i.e. it does not
create a model for given data. Instead, it simply stores data. This classifier works less
in training time but extensively during testing. One of the advantages of k-NN is that
it creates a model by grouping the nearest neighbors with similar properties [165].
Here, k represents the number of neighbors involved in decision making.
Experimentation is performed with 3-NN and 5-NN. Naïve Bayes is used as the third
classifier. It is a probabilistic learning algorithm. It is driven from Bayes‘ theorem as
given in Eq. 3.36.
(3.36)
where X represents the attribute in the feature vector, is the target class. The major
advantage of this classifier is efficiency. It takes less time for training when applied to
a large dataset [166]. The fourth classifier in this study is AdaBoost. It is a kind of
ensemble classifier based on the concept of boosting. It works in a cascading fashion
i.e., make sure that it should mainly focus on those training examples which are
misclassified by the previous weak classifiers [167]. The proposed method is
evaluated over publicly available benchmark dataset LIDC. The results and
61
experimentation details are discussed in chapter 4, which shows that the proposed
method has achieved remarkable improvements in the results.
3.4 Method III: Multistage Segmentation Model and SVM-
Ensemble for Precise Lung Nodule Detection
Human lung has a complex structure, due to which it becomes a challenging task for
the radiologists to identify the diseased region. This complex structure also remains a
challenge for automated lung cancer diagnosis methods. Therefore, a simple
segmentation method cannot produce good results. Considering these points, a
multistage segmentation method is presented in this section to improve the diagnosis
procedure. Fig 3.13 shows a workflow diagram of the proposed method.
Segmentation of lung nodules is a challenging phase in the detection process due to
the complex structure of the lung. Furthermore, handling of small, boundary attached
(juxta-pleural) and vessel attached (juxta-vascular) nodules is a big challenge. The
proposed technique addresses these issues in order to give a better detection method
for lung cancer. The lung region is extracted by applying corner seeded region
growing, combined with optimal thresholding. In addition to this, morphological
operations are applied in boundary smoothing, hole filling, and juxta-vascular nodule
extraction. In addition to segmentation, the selection of relevant features and a robust
classifier helps to improve accuracy and reduce false positives. Considering the
aforementioned geometric properties, along with 3D edge information are applied to
extract nodule candidates. Geometric Texture Features Descriptor (GTFD), followed
by Support Vector Machine based ensemble (SVM-ensemble) classification is
employed to identify actual nodules from the candidate set. SVM-ensemble classifier
is applied, which is based on three base classifiers, namely, SVM-linear, SVM-
polynomial and SVM-RBF (Radial Based Function). A publicly available dataset
LIDC-IDRI is used to evaluate the performance of the proposed method.
The main contributions of the proposed method are:
1. A multistage segmentation model is proposed for precise lung region extraction
and nodule candidate detection.
2. Differential evolution is used for threshold selection.
3. 3D morphological operation is used to separate juxta-vascular nodules from the
vessels.
62
4. Geometric Texture Feature Descriptor (GTFD) is used for the better
representation of nodule candidates.
5. SVM-ensemble classifier is proposed for classification of nodules and non-
nodules.
Fig 3.13: Workflow of the proposed method
3.4.1 Nodule Segmentation
Pulmonary nodule segmentation is an essential phase in the performance of an
automatic diagnostic system. During the diagnosis procedure, precise and accurate
segmentation of nodules impacts the performance. Segmentation of smaller nodules,
ranging from 3-30mm, juxta-pleural nodules, and vascular attached nodules is a
significant test for the segmentation method.
3.4.1.1 Lung Region Extraction
In the first step, the image background is removed by using corner seeded region
growing combined with thresholding approach. The thresholding is a straightforward
63
and efficient method for image segmentation. It performs segmentation on the basis of
pixel intensities. It is observed that simple thresholding is not useful for lung region
extraction due to overlapping between the intensities of background and some
sections of ROI. To overcome this issue, the background of input images is removed.
Furthermore, the threshold generated for one slice is not useful to other slices because
of its variation in the gray level for each slice as shown in Fig 3.14. Therefore, the
suggested segmentation technique used a combination of differential evolution based
optimal thresholding [168] and corner seeded region growing.
After the removal of image background, differential evolution based optimal
thresholding is used to identify the lung boundary and extraction of the lung region.
An initial threshold of -500HU is applied because most of the lung region lies in the
range of -950HU to -500HU. It is an iterative step in which the threshold is
recalculated in each iteration.
In an image, a histogram gives the probability distribution of gray levels. This
probability distribution is calculated by Eq. 3.37.
∑ ∑
√
[
]
(3.37)
The total number of categories within the image is represented by . and are
the probability and probability distribution functions within the category ,
respectively. and are the mean and standard deviation. To compute optimal
threshold, the overall probability error in two different categories is minimized by Eq.
3.38.
∫ ∫
(3.38)
This error is with respect to the threshold . The overall error is calculated as given
by Eq. 3.39.
64
∑
(3.39)
Henceforth, a threshold image is generated that contains the lung mask.
Morphological operations are applied to refine the lung boundary and extraction of
juxta-pleural nodules. Morphological closing is performed on the binary mask, where
a disk of size 25 pixels is taken as the structuring element. The process of closing also
includes some extra regions which can affect the accuracy of segmentation. To solve
this problem, morphological opening with the same structuring element, a disk of size
25 pixels, is applied. Fig 3.15 highlights the process of juxta-pleural nodule
extraction. Finally, the lung region is extracted by using the resultant mask and input
CT image. The whole procedure is repeated for all the slices of each scan. Fig 3.16
shows the step by step results of segmentation.
(a) (b)
Fig 3.14: Gray level distribution (a) input CT images (b) their respective histograms
65
(a) (b) © (d)
Fig 3.15: Juxta-pleural nodule extraction (a) input images with juxta-pleural nodules,
(b) corresponding lung masks missing pleural nodules, (c) boundary correction using
morphology, (d) extracted lungs with juxta-pleural nodules
66
(a) (b)
(c) (d)
Fig 3.16: Lung region extraction (a) input image, (b) background removed, (c) lung
mask, (d) extracted lung
3.4.1.2 Nodule Candidate Detection
After ROI extraction, the next step is to extract nodule candidates by eliminating
vessels and other unwanted objects. For this purpose, a combination of morphological
operations, edge detection, bounding box and shape information are applied to the
lung ROI for eliminating vessels and noise objects. The major issue in nodule
candidate detection is juxta-vascular nodules due to their attachment to the vessels
[169]. In most of the previously known techniques, these nodules are eliminated with
the removal of vessels. To avoid nodule elimination, 3D connectivity information is
used. As discussed earlier that a nodule is a 3D object, therefore, it must show its
existence in consecutive slices of a scan. Considering this property of nodule, the
presence of each object with its predecessor and successor slices is investigated. Let
is a candidate for a nodule pixel, where is the slice number and ,
represent the location of the pixel within that slice. Then 3D neighborhood is checked
by comparing the intensity of that pixel. Each pixel of slice has nine
neighborhoods in the slice and nine in the slice as shown in Fig 3.17. The
67
similar intensity pixels must be there, within the eighteen neighborhood pixels, either
in predecessor or in successor slices or in both.
Fig 3.17: 3D connectivity of candidate pixels
The morphological opening is used to separate juxta-vascular nodules from vessels.
The structuring element used for this purpose is a ‗sphere‘ of size 2 pixel radius,
which is effective in 3D lung structure. The nodule is a round shaped object, and the
use of a ‗sphere‘ as structuring element preserves the nodules and breaks the
connection of nodules and vessels. Afterwards, 3D connected component analysis is
performed, and regions with centers within 5mm are merged. Canny edge detection
method is used to identify the boundaries of each object [170]. It results in a large
number of candidate objects. To refine this set of objects, the nodule size and shape
information is taken into account. The nodule is a round shaped object that ranges
from 3mm to 30mm in size within the ROI. Considering these properties, the nodule
candidate set is defined on the basis of diameter, elongation, and circularity. The
objects with a diameter less than 3mm and greater than 30mm are removed and
considered to be unwanted objects as their sizes do not match with the size
specifications of nodules. In addition to diameter, those objects are also excluded,
which are too elongated, i.e., their major axis length is 2.25 times more than the minor
axis length. The third parameter is circularity, which defines the roundness of the
object. The roundness of an object ranges from 0 to 1, where 1 represents perfect
round. All those objects which have circularity less than 0.65 are also excluded from
the candidate set. The elongation and circularity threshold values are empirically
selected based on the observation that if circularity value is increased more than 0.65
and elongation value is decreased less than 2.25, this causes the removal of some of
the actual nodules. On these parameters, the nodule candidate set is refined, and
68
nodules are distinguished from other objects like vessels and noise objects as
presented in Fig 3.18. Some of the extracted nodule candidates are illustrated in Fig
3.19.
(a) (b)
Fig 3.18: Nodule candidate refinement (a) objects in ROI (b) nodule candidate regions
Fig 3.19: Extracted nodule candidates
3.4.2 Geometric Texture Feature Descriptor (GTFD)
In object detection and classification, precise and relevant features play a significant
role. In recent studies, a variety of features including geometric, texture, Local Binary
Patterns (LBP), wavelet, intensity, and gradient are used for the detection and
classification of nodules because a single type of feature vector cannot give an
accurate representation of objects, resulting in unsatisfactory performance. To
overcome these issues, researchers also used hybrid feature vectors by encapsulating
more types of features in a single feature vector. In this study, a GTFD by combining
texture and shape features in 2D and 3D is devised that successfully delineates the
nodules and non-nodules.
69
3.4.2.1 Geometric Features
Geometric features like circularity, area, and volume are the shape descriptors of an
object [171]. Therefore, these features are helpful to distinguish nodules from other
objects within the lung region. The median slice of a scan contains maximum
information of objects present in the scan. Therefore, 2D geometric features are
extracted from the median slices. However, 3D geometric features are extracted from
3D segmented objects. The features include area, radius, circularity, volume,
compactness, and elongation.
3.4.2.2 Texture Features
Texture features characterize the regions of images on the basis of their pixel
intensities [158]. Ten texture features, four 2D including mean, variance, skewness,
energy, and six 3D features including angular second movement (energy), contrast,
correlation, homogeneity, and entropy, are used in this study.
3.4.3 Ensemble Classification
SVM-ensemble classification is used to identify the actual nodules from a set of
candidate nodules. An ensemble classifier makes a final decision on the test dataset
derived from the output of individually trained base classifiers [172]. Let there be
base classifiers as: { } over a dataset and target . Each classifier is
trained and evaluated over the same test examples. The decisions of individual
classifiers are combined to make a final decision. Majority voting is a useful
combiner, in which decision about the class of a test example is made through the
number of votes, determined by Eq. 3.40.
∑
(3.40)
where assigns class labels to the test feature vector on the basis of maximum
votes given to a class and represents the total number of base classifiers. Let if there
are three base classifiers; two predict that the test example is of class A and one
predicts it in class B, then majority voting predicts it as class A. In this study, SVM is
used as a base classifier with three different kernels including linear, polynomial and
Gaussian RBF.
70
Fig 3.20: Workflow of SVM-ensemble for nodule classification
SVM is known to be a good classifier that learns easily and produces good
classification results. However, a single classifier may not learn all the parameters to
produce good global optimum result. To meet this limitation, an SVM based ensemble
classifier is developed for nodule classification. Fig 3.20 shows the workflow of
SVM-ensemble classifier for the proposed method to distinguish nodules and non-
nodules. During the training phase, each individual SVM based classifier is trained
over the labeled data provided for training. SVM-linear, SVM-polynomial, and SVM-
RBF are trained as base classifiers. The individual classifiers are aggregated on the
basis of voting. In the testing phase, an unlabeled test example is supplied as input to
all the individual classifiers simultaneously, and a final decision is made on the basis
of Eq. 3.40. The proposed method is evaluated over the largest publicly available
dataset LIDC-IDRI. The experimentation and results are discussed in the next chapter.
3.5 Method IV: Lung Nodule Detection and Classification based on
Geometric Fit in Parametric Form and Deep Learning
Although, the most recent studies have presented various lung nodule detection and
classification methods to improve the performance of the diagnosis process, but to
overcome the number of false positives remains a challenge. In addition,
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improvement in one stage of diagnosis method may not assert a strong impact on the
performance. In this study, these points are addressed, and the main focus is to reduce
the number of false positives by contributing at every stage of the diagnosis process.
The proposed lung nodule detection method to reduce false positives is illustrated in
Fig 3.21 consisting of four major steps. First, the optimal threshold is computed on
the basis of Fractional Order Darwinian Particle Swarm Optimization (FODPSO)
[173], which produces an initial binary lung mask. To improve the structure of this
mask for accurate segmentation, hole-filling and boundary smoothing are performed
by applying morphological operations. The lung region segmentation is performed on
the basis of this mask. After segmentation, a novel method based on the geometric fit
in parametric form [174], is proposed to detect circular shapes that can be a part of the
nodule candidate set. These candidates are refined on the basis of geometry and shape
properties of the nodules. In the third step, feature extraction and fusion are
performed. The geometric and texture features are extracted and fused to a hybrid
feature vector. A CT scan contains 3D information of lung along with the 2D
information. Taking into account this property, 3D texture and geometric features are
also extracted, along with 2D features. As a result, a hybrid feature vector is created.
In the fourth and final step, feature reduction and classification are performed. A two-
level stacked autoencoder (SA) is designed for feature reduction, and the softmax
activation function is trained to distinguish nodules and non-nodules.
The performance of the proposed method is evaluated over LIDC-IDRI dataset. The
performance factors, including sensitivity, specificity, accuracy, and number of
FPs/scan remain the primary focus of this study.
The main contribution of the proposed algorithm is given under:
1. FODPSO based optimal threshold to segment the lungs volume.
2. Proposed a novel method for nodule candidate detection using geometric fit in
parametric form by using geometric properties of nodules. Nodules pruning is
performed by using geometric information based rules.
3. Proposed a Hybrid Geometric Texture Feature (HGTF) descriptor for better
representation of nodule candidates, which improves the classification.
4. Deep learning for feature reduction and classification using stacked
autoencoder and softmax.
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Fig 3.21: Block diagram of the proposed method
3.5.1 Lung Volume Segmentation
The extraction of lung region from CT images is a much needed step before nodule
candidate detection. The segmentation separates the lung cavity and the surroundings,
including background and lung anatomy [175]. This removes the objects residing
outside the lung area that impedes the objects, residing outside the lung region, to
become part of the candidate set. The lung volume segmentation method, as shown in
Fig 3.22, consists of the following steps:
1. Initial lung mask extraction by applying FODPSO based optimal threshold.
2. Lung region extraction based on 3D connected component labeling.
3. Final lung mask generation by refining the lung boundary.
A lung CT scan can be categorized into two major regions; low intensity region and
high intensity region. These regions represent different anatomical structures. The
surrounding of the lung cavity lies in the high-density region, whereas the lung cavity
and air around the body belong to the low-density region. Before nodule candidate
detection, it is required to separate the lung volume from low-density regions because
the Region of Interest (ROI) is the lung volume. The major problem with CT data is
that all slices of a single patient contain a different portion of the lung; for example
initial slices contain only a small part of the lung, middle slices contain major lung
parts, and final slices also contain some part of the lung. Therefore, it is not possible
to have a single threshold to segment lung from all slices of a patient.
The lung tissues are usually in the range of -950 to -500 Hounsfield Units (HU) in CT
image, while the extent of dense area, including bone, blood, and the chest wall, is
• LIDC/IDRI Dataset
Input CT Images
• FODPSO Thresholding
• Hole Filling
• Boundary Smoothing
Lung Volume Segmentation
• Geometric Fit in Parametric Form
• Geometric Rule Base Refinement
Nodule Candidate Detection
• Geometric 2D/3D
• Texture
• Hybrid
Feature Extraction
• Autoencoder + Softmax based Deep Learning
Classification
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higher than -500 HU. On the other hand, air attenuation is at -1000 HU. Taking into
account these points, -500 HU is selected as an initial threshold. However, there is an
issue that middle slices of a 3D scan cannot be handled with this threshold as
compared to initial and last slices. A fixed threshold may not work well for all slices
due to this reason. Therefore, it is required to find out an optimal threshold to handle
the different types of slices. The most effective method to obtain the optimal threshold
is the one that maximizes the variance between the classes [176, 177] and can be
calculated using Eq. 3.41.
(3.41)
where and are the probabilities of foreground and background respectively. ,
and represents the mean gray level intensities of foreground, background and
complete image. An optimization method can be a useful tool in optimal threshold
selection. Considering this point, FODPSO is applied to find an optimal threshold that
can handle all types of slices.
Fig 3.22: Proposed lungs segmentation method
3.5.1.1 Fractional-Order Darwinian PSO based Optimal Threshold
Several variations of Particle Swarm Optimization (PSO) are introduced in recent
years, including Darwinian Particle Swarm Optimization (DPSO) [178], Fractional
Order Particle Swarm Optimization (FOPSO) [179] and FODPSO [173, 180]. The
FODPSO is based on Darwin‘s evolutionary theory. It is an extension of PSO but
with improved balance between exploration and exploitation with the help of
fractional order extension of FODPSO. The particles in FODPSO are known as
intelligent swarms interacting with each other and with the environment to achieve a
common goal. Using these principles, FODPSO overcomes the weakness of PSO,
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which get stuck in local optimization. The FODPSO overcomes the PSO weakness of
convergence. It controls convergence of swarm towards the solution. The FODPSO
executes several PSO algorithms in parallel on the same test problem. When a swarm
is stuck in local optima then search in that area is stopped, and the swarm is moved to
another area for the search. The swarms, which show better performance, are
rewarded, and the swarms with poor performance are punished. The algorithm is
initialized by a population of randomly created particles. The optimization process is
performed iteratively on the basis of pBest and gBest. Each swarm moves in search
space to a position , within the image intensity levels from to and
velocity . The practical best
and global best
information is used to update
the position and velocity of a particle and computed by Eq. 3.42 and Eq. 3.43.
( )( )
(3.42)
(3.43)
The fractional order controls the position and velocity by Grünwald–Letnikov
fractional derivative. The and are the weights that control local and global
performance. The and are random multipliers ranging from 0 to 1 and is the
time. On the basis of these equations, an optimum solution is computed. In the
threshold selection process, FODPSO particles are initialized with zero velocity and
positions are assigned within the gray levels. Each pixel is assigned a gray level
ranging from to , where is the lowest and is the maximum possible value
of a gray level. The total number of pixels for each gray level can be computed using
Eq. 3.44.
∑
∑
(3.44)
where is pixel count of the image and represents the number of gray levels. The
number of pixels in each category is computed by histogram .
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The probability distribution is calculated by Eq. 3.45.
∑ ∑
√
[
]
(3.45)
where an image contains number of categories. The is probability,
probability distribution, is mean and is variance of category . To compute the
optimal threshold, the overall probability error is minimized by the fitness function.
The threshold image is a binary image created using Eq. 3.46, on the basis of
final optimum threshold.
{
(3.46)
where, is the source image, represents maximum gray level in the image
and is optimum threshold. The problem space is explored by each particle for the
candidate solution. Eq. 3.47 gives the mathematical representation of particles, which
is used to construct the mixture of Gaussian probability function.
(3.47)
where and represent the mean and variance of particle within the search
space. The possible variance for each particle is computed and compared with other
particles within a swarm. The particle which achieves a high class variance is
considered as best performer and other particles move towards its direction. The
fractional extension improves exploitation behavior, which leads to an improved
convergence.
3.5.1.2 Fitness Function
The optimization is an iterative process that continues its execution until the fitness
criteria are achieved. In image segmentation, the goal of optimization is to minimize
the error between the original histogram and Gaussian functions shown in Eq. 3.48.
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∑ ( ) ( )
|(∑
) |
(3.48)
The total number of gray levels is represented by . The ( ) and ( ) represents
the probability and histogram of gray level respectively. The represents the
number of categories within the image. is the probability within the category and
is penalty associated with the probability of a category within the image. The
optimal threshold is computed through an iterative process, which continues till the
stopping criteria are fulfilled.
The initial segmented lung mask is generated using the optimal threshold computed
by FODPSO. This segmented mask contains the body and non-body voxels. To refine
this mask and extract the body voxels surrounded by non-body voxels, 3D connected
component labeling is applied. The 18 neighborhood pixels are used from predecessor
and successor slices of the 3D scan. The two largest regions are selected as the lung
body regions. Then hole filling is performed using morphological operations.
Fig 3.23 shows the segmentation results, which illustrates that FODPSO based
thresholding method worked well for lung volume extraction. The extracted lung
masks may not include the juxta-pleural nodules as shown in Fig 3.24(b), which affect
the diagnosis performance. The eight directional chain code analysis [181] is
performed for boundary correction, given in Algorithm 3.3, which results in juxta-
pleural nodule inclusion, as shown in Fig 3.24(c). The distance between the critical
points is calculated and compared with a nodule size and merged with those sections
which have distance less than nodule size. Fig 3.24(d) shows the final segmented
regions with juxta-pleural nodules.
77
(a) (b) (c)
Fig 3.23: Lung region extraction (a) input CT images (b) threshold images (c) extracted
lung regions.
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(a) (b) (c) (d)
Fig 3.24: Juxta-pleural nodule extraction (a) Images with pleura nodule, (b) initial lung
mask, (c) after boundary correction, (d) final ROI
Algorithm 3.3: Boundary Correction Algorithm
Input: Initial lung mask:
Output: Final lung mask after boundary correction:
Begin
1: = ► Compute boundary mask
2: ► Compute the gradient
3: ►Identify critical points on the basis of gradient
4: for each ►Process each pair of critical points
5: ► Segment length between x,y
6:
7:
8: if ►Check boundary segment is concave
9: if ► Compare with nodule size
10: = ► Update path from x to y
11: end if
12: end if
13: end for
14: ► Update lung mask
End
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3.5.2 Nodule Candidate Detection using Geometric Fit in Parametric
Form
The nodule candidate detection has prime importance in the automated diagnosis
process. This step requires the consideration of key properties of nodule candidates to
detect them accurately. The main target of this stage is to perform initial candidate
detection with 100% sensitivity, which reflects the inclusion of all nodules in the
candidate set. However, a major issue of this step is the inclusion of a large number of
non-nodules in the candidate set, which leads to high false positives. The higher
sensitivity with low false positives depicts the strength of a detection method. A novel
nodule candidate detection method, based on geometric fit in parametric form, is
proposed to address the aforementioned issues.
As discussed earlier, the nodules are round shape circular objects of different sizes
ranging from 3mm to 30mm [64]. Furthermore, in a CT scan, nodules appear to be the
brighter objects with maximum intensity at the center and mostly within dark
surroundings. Considering these points, the nodule detection strategy is developed, on
the basis of circular region detection using geometric fit in parametric form, by
considering the maximum intensity point as the centroid of the circle. The circle in
parametric form is represented by Eq. 3.49 and Eq. 3.50.
(3.49)
(3.50)
The geometric fit refers to the circles for which the sum of square distance from a
given points is minimal. Consider two points and , the distance between these
points is calculated as given in Eq. 3.51.
(3.51)
The minimum distance problem is solved using the Newton method, to minimize the
algebraic distance, which is expedient to get good initial values of , and . Then
minimum quadratic function is computed by minimizing , , and . The
Jacobian describes how this kind of change is transformed. To find area of the
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circle, the Cartesian coordinates are changed to polar coordinates, which results in the
following transformations given in Eq. 5.52 and Eq. 3.53.
∬
(3.52)
The resultant Jacobian is computed as given by Eq. 3.53.
|
| |
| (3.53)
The geometric fit is used to detect the nodule candidates from the segmented ROI of
each slice. The radius value from 1.5mm to 15mm is applied, to detect all sizes of
nodules. The contrast between each object, with center at and radius and its
surroundings is computed. The is computed using geometric fit in parametric
form. The object for which the sum of the squared distance to a reference point is
minimal is considered as the best fit. An object is considered best fit or geometric fit,
if its distance from the reference point, given in annotation, is less than 1.5 times its
radius [159]. In addition, a nodule has its presence in multiple slices of a scan;
therefore, in addition to the area, there is a need to calculate the volume as well. The
Jacobian for the 3D object is applied and is given in Eq. 3.54.
∭
∭ |
|
(3.54)
The , and are the coordinates of the actual object in scan, and , and are the
coordinates of the reference object within the annotation. The Gauss-Newton method
is an iterative process which produces the optimum values of , and . In this
process, all the circular objects are processed and checked over the criteria to become
a nodule candidate. For further refinement of the nodule candidate set, geometric rule
base refinement is performed. These properties are area, circularity, radius, perimeter
and elongation.
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Table 3.1: Threshold values for nodule candidate set refinement
Parameter Reference value
Radius From 1.5mm to 15 mm
Area 7.0 to 706.9
Perimeter 9.42mm to 94.25mm
Volume 14.13 to 141337
Circularity Greater than 0.65mm
Elongation Less than 2.25mm
Table 3.1 gives the reference values for the parameters used for the refinement of the
nodule candidate set. The objects with a radius less than 1.5mm and greater than
15mm are eliminated as their sizes do not match with the actual nodule properties.
The objects having area from 7.0 to 706.9 and perimeter from 9.42mm to
94.25mm are selected as nodule candidates. The circularity measures the roundness of
the objects, ranging from 0 to 1. The objects with circularity 1 are considered as
perfectly round. The objects in the candidate set with circularity less 0.65 are
excluded due to low roundness. The elongation is the ratio between major and minor
axis lengths. The objects having elongation greater than 2.25 are excluded from
candidate set because these are too elongated and considered as vessels. The threshold
values of circularity and elongation are selected experimentally. If the circularity
value is increased from 0.65 and the elongation value is decreased from 2.25, then
some of the actual nodules are also excluded resulting in low sensitivity. The nodule
candidates‘ regions are shown in Fig 3.25, and some of the extracted candidates are
shown in Fig 3.26.
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(a) (b)
Fig 3.25: Nodule candidate refinement (a) segmented ROIs, (b) the final candidate
regions
(a) (b)
Fig 3.26: Nodule candidates (a) nodules (b) non-nodules
3.5.3 Feature Extraction
Image features play a key role in the classification process. A precise feature set can
describe an image more accurately [84]. A feature vector is a numerical representation
of an image or object within the image. Similarly, a single type of feature may not be
a true representative of an object. To overcome this issue, there is a need to create a
feature vector that incorporates more than one type of features [104]. This type of
feature vector is known as a hybrid feature vector. In the proposed method, each
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feature vector incorporates 44 features, including 32 texture and 12 geometric
features. Besides the advantage of combining multiple features, there is a limitation
that it increases the dimensionality of a feature vector [182]. A high dimensional
feature vector increases the classifier training time. Feature reduction helps to
overcome this issue. A precise reduction curtails the feature vector without losing
important information. In the proposed method, a stacked autoencoder based deep
learning network is developed for the feature dimensionality reduction discussed in
section 3.5.4
3.5.3.1 Texture Features
The texture features characterize the image regions by their pixel intensities [183].
For texture analysis, Gray Tone Spatial Dependence Matrices (GTSDM) [184] is
computed, based on different angular ratios and distances among the neighborhood
pixels in 2D as well as 3D. These features also incorporate the spatial information
along with the texture, which includes the distance and angular relationships between
a nodule candidate and its surroundings [185]. At the first step, GTSDM matrix is
computed, and features are extracted by using this matrix. For 2D features, eight
neighborhoods are considered with angular information on 0, 45, 90 and 135 degrees
[186]. In a 3D space, a voxel has 26 neighborhoods, nine in each predecessor and
successor slices. Considering the spatial information along with their intensity levels,
the 3D texture features are extracted. Fig 3.27 (b) shows the 3D neighborhood of the
central voxel.
(a) (b)
Fig 3.27: Texture special information (a) pixels with neighborhood directions
(b) 3D neighborhoods
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The 2D texture features, including variance, skewness, Kurtosis, and energy are
computed in eight directions, then mean and range for each feature value is computed.
In addition to 2D features, six 3D features, including angular second movement,
correlation, inertia, absolute value, inverse difference and entropy are computed.
Suppose is a feature matrix, is the cell of that matrix containing the feature
value in direction. For 2D features, four feature values in eight directions are
computed. Then mean and range of each feature value is computed, which results in a
1x8 sized 2D feature vector. The association of the voxels in 3D space is different as
compared to the 2D plane which requires that rather than computing features in all 26
neighborhoods, try different combinations for sampling the data. Considering these
points, six 3D features in 18 and 26 neighbors are computed. After that, mean and
range for each feature are computed same, as 2D features, which results in a 1x24
feature vector. Finally, 32 texture features are extracted including eight 2D and twenty
four 3D features.
3.5.3.2 Geometric Features
The second group of features is the geometric features that describe the shape
information of the nodules. These are computed from the segmented candidate
objects. It is a very useful descriptor to distinguish true positives from false positives.
To distinguish nodules from other objects, it is desirable to use shape information.
The geometric shape descriptor includes 2D as well as 3D geometric features [187].
The 2D geometric features include area, radius, perimeter, and circularity. In addition
to 2D features, 3D features are also very important for nodule classification because a
nodule is a 3D object which has its existence in multiple adjacent slices. The 3D
geometric features include volume, compactness, elongation, bounding box
dimensions and principal axis lengths. The principal axis length includes two features,
i.e. major and minor axis length. The bounding box dimensions include three features
in each dimension x, y, z-axis, which results in eight 3D geometric features. As a
result, 12 geometric features are extracted.
3.5.3.3 Feature Fusion to Create Hybrid Feature Vector
Feature fusion is an encapsulation of different types of feature vectors into a single
feature vector [188]. The feature fusion of multiple features improves the
classification accuracy because different objects within a dataset have different
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properties with respect to the feature types [189]. In the proposed method, two level
serial fusion is applied, as shown in Fig 3.28. At the first level, 2D and 3D geometric
and texture features are fused, which results in geometric and texture feature vectors.
On the second level of fusion, these texture and geometric feature vectors are fused to
form a hybrid geometric texture feature (HGTF). The fusion of these individual
vectors is carried out on serial basis, to form a hybrid vector based on Eq. 3.55 to Eq.
3.57, by fusion of these vectors.
(3.55)
(3.56)
(3.57)
where, and are fused geometric and texture feature vectors respectively, and
is the final hybrid feature vector. represents the serial fusion.
Fig 3.28: Feature fusion for the hybrid feature vector
3.5.4 Stacked Sparse Autoencoder and Softmax Based Feature
Optimization and Classification
Classification is the final step of the diagnosis process which distinguishes nodules
from other objects. The classifier is trained on labeled examples and in the testing
phase, unlabeled data is provided to a trained classifier, which classifies it to a
relevant class. Feature reduction is a process that improves the performance of a
classifier by selecting more appropriate feature subset from a large feature set. The
commonly used methods for feature reduction are principal component analysis
(PCA), genetic optimization and PSO based optimization [105]. Deep learning is an
important area of research that provides a platform for different classification
86
problems. The key feature of deep learning is to create a large neural network that
improves the accuracy of classification and prediction problems. An autoencoder is a
kind of deep neural network which has an important role in the feature reduction and
optimization problems. It is an unsupervised three layer network with same sized
input and output layers. Its transformation of input to the hidden layer is known as
encoding, and from hidden to the output layer is called decoding [190]. It maps the
high dimensional feature vector to the lower dimensional feature vector.
However, a single autoencoder may not be very accurate. To overcome this
shortcoming, a deep neural network is proposed including two staged stacked
autoencoder for feature reduction and softmax for classification. A deep autoencoder
takes a high dimensional input feature space and then reduce it by a series of smaller
hidden layers. Reduction of data to lower dimension improves the performance of a
classifier. Autoencoder maps input feature vector to hidden layer
where k<n. That hidden layer is a new feature representation. When the size of the
hidden layer is less than the input layer then autoencoder maps high dimensional input
feature vector to low dimensional feature vector. These features are then used as input
to the second hidden layer of autoencoder. In this way a two-level stacked
autoencoder performs feature optimization. The network is trained with scaled
conjugate gradient [191]. The Eq. 3.58 and Eq. 3.59 give the update rules and
activation functions for the training of network.
(3.58)
where,
is the bias of the layer, is the learning rate and is the additional
learning rate.
(∑
) (3.59)
where
is the neuron of the layer,
are the synaptic weights, is the
input to the neuron and is the bias. Fig 3.29 shows the network structure of the
proposed deep net. During the training of network, each input feature is passed to
all the hidden layers in a feedforward manner and then to the output layer to compute
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. The error is computed, which gives the deviation from input to output. This error
is backpropagated to perform weight updation. The first layer receives HGTF feature
vector as an input and passes it to the first autoencoder, which encodes these features
and produces a new feature vector. This new feature vector is passed to the second
autoencoder as an input following the same procedure. In this way, a stacked
autoencoder is constructed. Finally, the low dimensional feature vector is given to the
softmax classifier to distinguish nodules and non-nodules. In contrast to autoencoder,
softmax is based on supervised learning. The softmax is a probability distribution
function that maps a K-dimensional vector of arbitrary values to the K-dimensional
real values computed by Eq. 3.60.
∑
(3.60)
where ∑ works as a regularization parameter.
Fig 3.29: Stacked autoencoder for feature optimization
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3.6 Summary
This chapter presents four proposed methods to detect and classify lung nodules,
which provide the second opinion to the radiologists during the diagnosis process.
The primary focus of these methods is to reduce the number of false positives along
with high sensitivity. The precise segmentation of the lung region and nodule
candidates improves the detection process. Similarly, precise features and robust
classifier identify the actual nodules from the candidate set. In the proposed methods,
these points are considered. Lung region is extracted with the target that all the juxta-
pleural nodules must become part of ROI. In the nodule segmentation process, precise
segmentation of juxta-vascular nodules remains the primary focus. To achieve these
targets 3D image information is also considered along with the 2D information. For
classification of nodules and non-nodules, different types of 2D, as well as 3D
features including shape, texture, intensity and gradient features, are extracted.
Finally, instead of relying on state of the art classifiers, ensemble classifiers, and deep
learning based feature reduction and classification are techniques proposed. The
experimental setup, implementation details, and the results of these methods are
discussed in the next chapter.
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Chapter 4
Results and Discussion
90
4.1 Introduction
In this chapter, the performance of the proposed methods is evaluated. The following
subsections contain a detailed discussion of the experimental setup, dataset
description, and the annotation details, quantification measures, experiments, and
comparison of proposed methods against the methods reported in the literature. The
experimentation statistics are illustrated in Fig 4.1, including the dataset details, data
distribution, and performance measures used to evaluate the significance of the
proposed methods.
Fig 4.1: Experimentation statistics of all proposed methods
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4.2 Method I: Lung Nodule Detection using Polygon Approximation
and Hybrid Features from Lung CT Images
In this study, an authentic publicly available dataset LIDC is used to evaluate the
performance of the proposed method. It contains 3D scans, each containing more than
100 slices with slice thickness between 1.25mm to 3mm and dimension of 512×512
pixels. Each image has a 12-bit grayscale resolution with the pixel size ranging from
0.5mm to 0.8mm. Moreover, along with the image data, another repository of XML
files is also provided for the convenience of researchers to get details about images
available in the dataset. In the present case, all LIDC scans are used in
experimentation. Further, the classification of nodules is performed using linear, RBF
and polynomial kernels. The performance of classifiers is evaluated over different
distributions of data into training and testing ratio.
4.2.1 Experimentation and Results
MATLAB 2015a is used as an implementation tool. The results using SVM with
different kernels are shown in the tables from Table 4.1 to Table 4.3. The Linear
kernel is applied with the division of dataset in training and testing with the ratios of
20-80, 30-70, 50-50 and 30-70. In the case of linear kernel, it can be seen from Table
4.1 that the proposed technique produced 83.4% accuracy but with a very high false
positive rate.
The second kernel used is polynomial. The proposed technique is evaluated with
polynomials of degree 2, 3, 4, 5 and 6, respectively, with the same distribution of test
and training examples used in the linear kernel. It can be observed from Table 4.2 that
the best results produced by the polynomial kernel of degree 5 are on 70-30
distribution for training and testing, respectively.
Table 4.1: Results produced by SVM linear kernel
Training-Testing Accuracy(%) Sensitivity(%) Specificity(%) FPs/Scan AUC
80-20 83.4 85.6 74.3 25.7 0.82
70-30 82.1 85.2 78.7 22.3 0.81
50-50 78.2 75.4 71.4 28.6 0.78
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Table 4.2: Results produced by SVM polynomial kernel
Training-Testing Accuracy(%) Sensitivity(%) Specificity(%) FPs/Scan AUC
80-20 94.1 95.3 93.2 6.8 0.97
70-30 98.8 97.7 96.2 3.8 0.99
50-50 91.2 89.6 87.5 12.5 0.94
The Gaussian RBF kernel is applied to train and test the SVM classifier. The
accuracy, sensitivity, specificity, and FPs/scan are computed. The RBF kernel
produced the best results, as reported in Table 4.3, with 70-30 distribution of training
and test data, respectively.
Table 4.3: Results produced by SVM-RBF
Training-Testing Accuracy(%) Sensitivity(%) Specificity(%) FPs/Scan AUC
80-20 96.3 96.0 95.8 4.9 0.98
70-30 98.8 97.8 97.2 3.7 0.99
50-50 95.2 89.3 95.3 6.1 0.98
Table 4.4 shows the best results obtained by all three kernels used in the proposed
technique. The linear kernel produced the worst performance with 83.4% accuracy
and 85.6% sensitivity with an FPs/scan of 25.7. It is due to the limitation of the linear
kernel in handling the high dimensional data. On the other hand, the RBF kernel has
given an excellent performance with an accuracy of 98.8% and 97.8% sensitivity with
very low FPs/scan of 3.7. Here it is clear that SVM-RBF kernel produces best results.
Figure 4.2 shows the comparison of classification results graphically.
Table 4.4: Best results by SVM classifier with different kernels
Kernel Training-Testing Accuracy(%) Sensitivity(%) Specificity(%) FPs/Scan AUC
Linear 70-30 83.4 85.6 74.3 25.7 0.82
Polynomial 70-30 98.8 97.7 96.2 3.8 0.99
RBF 70-30 98.8 97.8 97.2 3.7 0.99
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Fig 4.2: Comparison charts of classification results
4.2.2 Comparison of Proposed Technique with Existing Techniques
The most important factor in performance evaluation is to compare the results with
existing methods in the same domain. It is a challenging task due to the heterogeneity
in evaluation parameters like dataset, nodule size, number of scans and result metrics.
However, some common parameters are identified for comparison. In this regard, six
parameters are used, including dataset, accuracy, sensitivity, specificity FPs/scan and
the size of the nodules considered for experimentation. The most important among
these parameters is the number of false positives because it severely affects the results
of a diagnosis system. Reducing false positives may lead to compromise the
sensitivity, specificity, and accuracy. A good diagnosis system generates a minimum
number of false positives without compromising over the score of other parameters.
Therefore, a good combination of these scores shows the strength of a diagnosis
system.
94
Ta
ble
4.5
: Com
pariso
n o
f the p
rop
ose
d m
ethod
with
the resu
lts of ex
isting
techn
iqu
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Meth
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ear
Da
taset
Acc
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) S
ensitiv
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9]
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Shaukat, et al. [1
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Fig 4.3: Comparison chart of proposed and existing system results
96
Table 4.5 gives a valuable comparison of the proposed technique with other existing
methods in the same domain. Table 4.5 and Figure 4.3 show that the proposed method
outperformed the existing methods in most of the cases. On the other hand, the
proposed technique‘s results are comparable with a few existing methods like Messay,
et al. [89] produced less FPs/scan than the proposed method, but in case of sensitivity,
there is a huge difference. Cascio, et al. [94], also produced better specificity but
lacked in comparison of sensitivity and FPs/scan, which are key parameters to
evaluate a diagnosis method.
4.3 Method II: 3D Nodule Candidate Detection Supported by
Hybrid Features to Reduce False Positives
The details of the dataset, performance evaluation parameters, experimentation, and
comparison of results with existing techniques are discussed in this section.
4.3.1 Dataset and Evaluation Parameters
To evaluate the significance of a diagnosis system, it is very important to evaluate on
some standard benchmarks. There are two key factors involved in performance
evaluation. First is to test the proposed method on some standard dataset and
secondly, use some standard evaluation parameters. Henceforth, the images for
experimentation are taken from a standard dataset LIDC [31]. It is publicly available
at National Biomedical Imaging Archive (NBIA). It contains 84 scans of which 58
scans contain nodules. Each image of this dataset has 512x512 dimensions. Each scan
contains more than 100 images of a patient [34, 35]. All the scans are used in this
study for performance evaluation. The evaluation parameters have a key role in
comparing the performance of different methods. There is a need to set some standard
parameters. In this study, six parameters, including sensitivity, specificity, accuracy,
FPs/scan, nodule size and area under the ROC curve are considered as evaluation
parameters. Actual nodules which are correctly identified by the classifier represented
as TP, whereas TN represents actual nodules which wrongly classified as non-
nodules. FP is the number of non-nodules classified as nodules and FN represents
those candidates, which are incorrectly classified as nodules.
97
4.3.2 Experimentation
Several experiments are performed over a different combination of features and
classifiers. Four different classifiers are used, i.e. SVM, k-NN, Naïve Bayes and
AdaBoost, to distinguish nodules and non-nodules. During this process, seven feature
vectors are created as follows:
V1 = Texture features
V2 = Geometric features
V3 = HOG-PCA
V4 = Geometric +Texture
V5 = Geometric + HOG-PCA
V6 = Texture + HOG-PCA
V7 = Geometric + Texture +HOG-PCA
The holdout cross validation is used for the classification. The classifiers are trained
and tested on 80-20, 70-30, 60-40 and 50-50 distribution, respectively. To avoid the
biasness, experiments are repeated ten times, and the mean score is reported in tables
from Table 4.6 to Table 4.12.
Table 4.6: Results with the geometric feature vector
Classifier Accuracy(%) Sensitivity(%) Specificity(%) AUC
SVM-Linear 86.5 86.2 71.1 0.74
SVM-RBF 93.2 90.1 90.4 0.87
SVM-poly-2 90.1 89.5 86.2 0.86
SVM-poly-3 88.9 88.0 84.3 0.86
3-NN 81.0 83.0 74.7 0.84
5-NN 75.4 77.6 67.1 0.73
Naïve Bayes 94.2 95.7 90.5 0.93
AdaBoost-50 95.5 90.6 93.7 0.95
AdaBoost-60 95.7 93.0 94.6 0.95
AdaBoost-70 96.2 95.4 95.0 0.96
AdaBoost-80 97.0 96.1 95.7 0.96
AdaBoost-90 97.3 96.9 96.5 0.97
AdaBoost-100 97.3 96.8 96.4 0.97
98
Table 4.7: Results with the texture feature vector
Classifier Accuracy(%) Sensitivity(%) Specificity(%) AUC
SVM-Linear 82.1 85.2 69.7 0.74
SVM-RBF 93.1 90.2 87.4 0.88
SVM-poly-2 90.4 89.4 86.9 0.87
SVM-poly-3 91.2 89.6 87.5 0.87
3-NN 78.9 80.9 73.6 0.86
5-NN 74.2 74.5 65.8 0.72
Naïve Bayes 90.1 90.6 89.4 0.92
AdaBoost-50 92.2 88.6 91.5 0.95
AdaBoost-60 93.6 90.9 91.3 0.95
AdaBoost-70 94.1 93.3 93.1 0.96
AdaBoost-80 94.7 94.2 93.3 0.96
AdaBoost-90 95.2 94.8 94.2 0.97
AdaBoost-100 95.2 94.8 94.0 0.97
Table 4.8: Results with HOG-PCA feature vector
Classifier Accuracy(%) Sensitivity(%) Specificity(%) AUC
SVM-Linear 79.1 80.4 69.1 0.72
SVM-RBF 83.4 85.6 83.2 0.86
SVM-poly-2 85.0 82.2 84.1 0.87
SVM-poly-3 86.2 84.4 84.3 0.88
3-NN 77.9 79.9 72.6 0.82
5-NN 72.7 70.3 66.1 0.70
Naïve Bayes 91.1 92.6 87.4 0.92
AdaBoost-50 92.4 87.5 90.6 0.91
AdaBoost-60 92.6 89.9 91.5 0.92
AdaBoost-70 93.1 92.3 91.9 0.92
AdaBoost-80 93.9 93.0 92.6 0.93
AdaBoost-90 94.2 93.8 93.4 0.95
AdaBoost-100 94.2 93.7 93.3 0.95
99
Table 4.9: Results with texture + geometric feature vector
Classifier Accuracy(%) Sensitivity(%) Specificity(%) AUC
SVM-Linear 91.4 90.6 85.3 0.90
SVM-RBF 98.3 97.7 97.2 0.99
SVM-poly-2 94.2 93.6 90.0 0.96
SVM-poly-3 95.8 95.2 93.2 0.97
3-NN 82.1 84.1 73.8 0.86
5-NN 78.3 79.5 68.1 0.74
Naïve Bayesian 95.0 96.3 90.9 0.93
AdaBoost-50 96.6 91.7 94.8 0.96
AdaBoost-60 96.8 94.1 95.7 0.97
AdaBoost-70 97.3 96.5 96.1 0.97
AdaBoost-80 98.1 97.2 96.8 0.98
AdaBoost-90 98.4 98.0 97.6 0.99
AdaBoost-100 98.4 97.9 97.5 0.99
Table 4.10: Results with texture + HOG-PCA feature vector
Classifier Accuracy(%) Sensitivity(%) Specificity(%) AUC
SVM-Linear 87.2 82.4 83.3 0.85
SVM-RBF 96.2 93.3 93.9 0.97
SVM-poly-2 93.5 92.4 89.8 0.96
SVM-poly-3 93.9 94.0 92.4 0.97
3-NN 81.3 82.2 72.4 0.86
5-NN 77.2 78.1 68.2 0.74
Naïve Bayesian 94.8 95.4 90.1 0.93
AdaBoost-50 96.1 91.3 94.3 0.96
AdaBoost-60 96.3 93.6 95.1 0.97
AdaBoost-70 96.7 96.2 95.5 0.98
AdaBoost-80 97.5 96.6 96.4 0.98
AdaBoost-90 97.9 97.5 97.1 0.99
AdaBoost-100 97.8 97.5 96.9. 0.99
100
Table 4.11: Results with geometric + HOG-PCA feature vector
Classifier Accuracy(%) Sensitivity(%) Specificity(%) AUC
SVM-Linear 90.2 89.7 83.8 0.89
SVM-RBF 96.7 93.8 94.1 0.97
SVM-poly-2 93.7 92.7 89.9 0.96
SVM-poly-3 94.6 94.3 92.9 0.96
3-NN 82.0 83.7 73.4 0.86
5-NN 76.6 76.1 67.7 0.73
Naïve Bayesian 96.8 97.7 91.8 0.93
AdaBoost-50 96.4 91.6 94.6 0.97
AdaBoost-60 96.7 93.7 95.3 0.97
AdaBoost-70 97.2 96.3 95.7 0.97
AdaBoost-80 97.7 96.7 96.6 0.98
AdaBoost-90 98.2 97.8 97.3 0.99
AdaBoost-100 98.0 97.6 97.2 0.99
Table 4.12: Results with geometric + texture + HOG-PCA feature vector
Classifier Accuracy(%) Sensitivity(%) Specificity(%) AUC
SVM-Linear 91.5 90.5 88.1 0.90
SVM-RBF 98.6 97.9 97.4 0.99
SVM-poly-2 95.2 96.0 94.3 0.98
SVM-poly-3 97.6 97.2 96.2 0.98
3-NN 83.4 85.6 74.3 0.87
5-NN 79.0 80.9 70.1 0.78
Naïve Bayesian 93.3 94.8 87.6 0.89
AdaBoost-50 96.9 92.0 95.1 0.97
AdaBoost-60 97.1 94.3 96.0 0.98
AdaBoost-70 97.6 96.8 96.3 0.98
AdaBoost-80 98.3 97.4 97.1 0.99
AdaBoost-90 99.2 98.3 98.0 0.99
AdaBoost-100 99.2 98.3 97.8 0.99
101
SVM is applied with different three kernels, including linear, RBF and polynomial of
degree 2 and 3. K-NN is applied with k=3and k=5 neighborhoods. Similarly,
AdaBoost is also used with different variations in the number of iterations. The
iterations of AdaBoost represent the number of weak classifiers used in
experimentation. The increase in the number of weak classifiers is directly
proportional to the time complexity, which leads to slow down the execution process.
On the other hand, a lower number of weak classifiers results in reduced accuracy.
Therefore, it is much needed to select an optimum value of weak classifiers resulting
in good accuracy with less computation time. The highest accuracy is achieved over
90 iterations. Figure 4.4 shows the ROC curve for a different number of iterations.
For implementation, MATLAB 2016a is used.
The tables from Table 4.6 to Table 4.8 illustrate the best results produced by each
classifier with a single type of feature vector. In addition to that, hybrid feature
vectors V4, V5, V6, and V7 are also used for classification. The results with these
feature vectors are shown in Table 4.9 and Table 4.12. SVM produced the best results
with 70%-30% data distribution for training and testing, respectively. The best results
are with RBF kernel, i.e., 93.1% accuracy, 90.2% sensitivity and 87.4% specificity
over texture features, as shown in Table 4.6; 93.2% accuracy, 90.1% sensitivity and
90.4% specificity with geometric features reported in Table 4.7. Similarly, 83.4%
accuracy, 85.6% sensitivity and 83.2% specificity with HOG-PCA reported in Table
4.8. On the other hand, Table 4.9 to Table 12 show results with hybrid features and
best results are with feature vector V7, which is a combination of all three types of
features including geometric, texture and HOG-PCA, reported in Table 4.10 and
Table 4.11. These results are 99.2% of accuracy, 98.3% of sensitivity and 98.0% of
specificity with 3.3 FPs/scan. The results show that a single type of feature vector
cannot be a true representative of nodules. It is clear that results with hybrid feature
vectors are better than the single type of features. The comparison of results, reported
from Table 4.6 to Table 4.8, illustrates that in a single type of feature vector, the
geometric features are better representatives of nodules. However, one type of feature
vector is not the best descriptor of the nodules.
102
(a)
(b)
Fig 4.4: ROC analysis (a) AdaBoost results with different number of iterations over V7
(b) SVM results over hybrid feature vector V7
103
(a)
(b)
Fig 4.5: ROC analysis (a) results using feature vector V7 (b) results using feature vector
V5
104
Further, Fig 4.4 and Fig 4.5 give a valuable performance comparison of all the
classifiers with different feature vectors. It shows results over feature vector consists
of texture and geometric and HOG-PCA features. These results show that AdaBoost
has better performance over the other classifiers. Fig 4.5 (a) shows the results of
optimum feature vector, including intensity, shape and HOG features.
It is clear that the results are improved using optimum feature vectors for AdaBoost
and SVM. However, the performance is desecrated in the case of Naïve Bayes. Fig
4.5 shows that AdaBoost outperforms the other classifiers. The ROC values of
AdaBoost, SVM and Naïve Bayes and k-NN are 0.99, 0.99, 0.87 and 0.87,
respectively.
4.3.3 Comparison of Proposed Method with Existing Techniques
A brief comparison is given in Table 4.13, and it shows a clear picture that the
proposed method has produced better performance than the existing techniques in
most of the parameters. Messay, et al. [89] produced less number of false positives
than the proposed method but lacked in accuracy and sensitivity. Magalhães Barros
Netto, et al. [93] produced a remarkable score in FPs/scan, but that score is over a
smaller subset of the dataset. They have used only 29 scans of LIDC dataset. Besides
false positives, they have a very low score in other parameters.
Dehmeshki, et al. [49] evaluated their technique over a private dataset instead of the
standard dataset and nodule size of 3-20mm with high FPs/scan. The recently
developed deep learning methods [118, 119, 127] for lung nodule detection has not
shown promising results. Nibali, et al. [118] also pointed out that deep learning is
facing challenges in automated diagnosis. The comparison of results depicts that the
proposed method has reduced the number of FPs/scan with good accuracy, sensitivity,
and specificity.
105
Ta
ble
4.1
3: R
esults c
om
pariso
n o
f the p
rop
osed
meth
od
with
existin
g tech
niq
ues
Meth
od
olo
gy
Y
ear
Da
tasets
Acc
ura
cy(%
) S
ensitiv
ity(%
) S
pec
ificity(%
) F
Ps/S
can
N
od
ule S
ize(mm
)
Deh
mesh
ki, et al. [4
9]
20
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y, et al. [8
9]
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C
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alhães B
arros N
etto, et al. [9
3]
20
12
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i and
Cho
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] 2
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villa a
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Gu
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i [10
3]
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, et al. [61
] 2
01
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LID
C
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–3
0
Taşcı an
d U
ğur [5
5]
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15
] 2
01
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Nib
ali, et al. [11
8]
20
17
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Do
u, et al. [1
19
] 2
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Jaffar, et al. [12
4]
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97
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Ali, et al. [1
27
] 2
01
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LID
C-ID
RI
64
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58
.9
55
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-30
Huid
rom
, et al. [13
0]
20
19
LID
C-ID
RI
93
.23
9
3.2
6
93
.2
- 3
-30
Shaukat, et al. [1
31
] 2
01
9
LID
C
93
.7
95
.5
94
.28
5
.72
3-3
0
Pro
po
sed M
etho
d
L
IDC
9
9.2
9
8.3
9
8.0
3
.3
3-3
0
105
106
4.4 Method III: Multistage Segmentation Model and SVM-
Ensemble for Precise Lung Nodule Detection
In this method, an authentic publicly available dataset LIDC-IDRI [34] is used to
evaluate the performance of the proposed method. The tube current range, from 40
milliampere-seconds (mA.s) to 627mA.s, is used for image acquisition. The tube
potential energies have a range from 120kV to 140kV. The low-dose scans are more
effective to identify small size lung nodules [192]. The dataset contains 1018 CT
scans of 1010 patients collected from 7 academic centers. Each scan contains
approximately 100 to 400 slices with slice thickness between 0.6mm to 5mm and
dimension of 512×512 pixels. Each image has a 12-bit grayscale resolution with the
pixel size ranging from 0.5mm to 0.8mm. Out of 1018 cases, 121 cases have slice
thickness ≥ 3mm, which is not suitable for the computer based diagnosis [193]. In
addition to this, there are nine cases that have inconsistent slice spacing. By excluding
these cases, a total of 888 scans are left for the automated diagnosis. Every scan of the
dataset is reviewed by four experienced radiologists for nodule annotation. The
annotation information for each scan is provided in XML format. All the radiologists
have a consensus on 777 nodules. In this study, those nodules are considered for
experimentations, which are marked by all the four radiologists. Around 45% scans of
dataset contain no nodules, which are annotated by all the four radiologists, and some
scans have multiple nodules. Considering these facts, those scans are excluded, which
have zero nodules, and those scans are selected, which contain the number of nodules
in the range of 1-8. As a result, 250 scans are finally selected, with 567 nodules.
Candidate nodules are classified according to annotations provided by the expert
radiologists. If a nodule candidate meets the criteria, it is considered as hit and
classified as a nodule. On the other hand, if it does not meet the criteria, it is taken as
a miss and considered as a non-nodule. A candidate is considered hit if its radius is
0.8 to 1.5 times of the actual nodule or its distance with reference to any
corresponding annotated nodule is less than 1.5 times the radius of that nodule [54].
The performance of classifiers is checked over different distributions of data into
training and testing ratio.
107
4.4.1 Experimentation and Results
MATLAB 2016a is used as an implementation tool. Three different distributions of
data are used as 80-20, 70-30 and 50-50 percent for training and testing purposes,
respectively. The hold-out validation is used for training and testing the classifiers.
The process is repeated 10 times to avoid the biasness and average score is computed.
Various experiments are executed with different combinations of feature sets. The
results are shown in Table 4.14 to Table 4.16 with geometric, texture and GTFD
features, respectively. SVM is trained with three different kernels, namely SVM-
linear, SVM-polynomial, and SVM-RBF. The output of individual classifiers over the
test examples is evaluated. Majority voting is applied to make a final decision by
SVM-ensemble. From Table 4.14 to Table 4.16, it is clear that SVM-ensemble has
produced significantly better results as compared to individual classifiers.
Table 4.14: Results with geometric features
Classifier Training-Testing Sensitivity(%) Specificity(%) Accuracy(%) AUC
SVM-linear 80-20 85.2 72.1 82.3 0.90
70-30 85.7 73.2 82.5 0.90
50-50 83.3 70.3 79.8 0.90
SVM-polynomial 80-20 88.6 87.3 90.5 0.93
70-30 90.3 88.4 91.9 0.93
50-50 87.2 84.7 88.8 0.93
SVM-RBF 80-20 92.7 90.9 92.5 0.96
70-30 94.6 92.2 93.4 0.96
50-50 90.6 88.6 89.2 0.96
SVM-ensemble 80-20 94.2 91.4 93.2 0.98
70-30 95.1 93.7 94.4 0.99
50-50 92.0 89.8 92.2 0.98
Performance of the proposed method is evaluated with the individual as well as
combined feature sets. The results show that combined feature vectors produced a
better performance as compared to a single feature type.
108
Table 4.15: Results with texture features
Classifier Training-Testing Sensitivity(%) Specificity(%) Accuracy(%) AUC
SVM-linear 80-20 84.5 72.1 81.8 0.88
70-30 85.2 72.7 82.1 0.88
50-50 83.1 69.9 79.3 0.87
SVM-polynomial 80-20 88.1 86.4 89.8 0.91
70-30 89.6 87.5 91.2 0.91
50-50 86.8 84.2 88.3 0.90
SVM-RBF 80-20 92.3 90.2 91.9 0.94
70-30 93.9 91.8 92.7 0.94
50-50 90.0 87.9 88.4 0.94
SVM-ensemble 80-20 93.4 90.9 92.2 0.97
70-30 94.8 93.2 93.7 0.97
50-50 91.3 89.1 90.8 0.97
Table 4.16: Results with GTFD
Classifier Training-Testing Sensitivity(%) Specificity(%) Accuracy(%) AUC
SVM-linear 80-20 88.2 75.6 85.3 0.93
70-30 89.1 76.7 85.8 0.93
50-50 86.4 73.2 83.1 0.92
SVM-polynomial 80-20 91.7 90.6 93.9 0.98
70-30 93.6 91.2 95.3 0.98
50-50 91.0 88.9 92.8 0.97
SVM-RBF 80-20 96.9 96.7 97.4 0.99
70-30 97.7 97.4 98.3 0.99
50-50 95.8 95.8 96.8 0.98
SVM-ensemble 80-20 98.0 97.6 98.4 0.99
70-30 98.6 98.2 99.0 0.99
50-50 97.2 96.0 97.3 0.99
109
The performance with texture features is lower than the geometric features. However,
when GTFD is used by encapsulating both geometric and texture features, better
results are obtained as compared to a single type of feature vector. Furthermore, best
results are shown when experimentation is done over the combination of 70-30, i.e.,
70% training and 30% testing.
Table 4.16 depicts that GTFD presented better results than individual features. In
comparison to Table 4.14 and Table 4.15, it gives a clear picture that geometric
features are better descriptors of nodules as compared to texture features. However,
on an individual basis, they cannot produce the best results. Fig 4.6 illustrates a
receiver operating characteristic (ROC) curve for the results of classification. It is
evident that ensemble based SVM shows better results as compared to RBF,
polynomial and linear kernels.
Fig 4.6: ROC curve with GTFD feature vector
The nodule candidate detection process comprises of two stages. In the first stage,
66021 nodule candidates are detected with 261.8 FPs/scan. At this step, all 567 actual
nodules are also recognized with 100% sensitivity. The second stage is related to
candidate refinement on the basis of nodule size and shape information. In this stage,
the number of candidates is reduced to 46176, and FPs/Scan is decreased to 182.5
with 45614 total false positives. However, this refinement phase has missed 5 actual
nodules and sensitivity is reduced to 99.1%.
The classification phase, based on SVM-ensemble, reduces the number of false
positives to 838, which is 3.4/scan. The free-response receiver operating characteristic
110
(FROC) curve in Fig 4.7 (a) illustrates the number for FPs/scan produced by four
classifiers. The SVM with linear kernel delivers 9.6 FPs/scan with 89.1% sensitivity,
SVM-polynomial produces 6.4 FPs/scan with 93.6% sensitivity, and SVM-RBF
shows comparatively good results with 4.2 FPs/scan and sensitivity of 97.7%.
However, SVM-ensemble reduces the FPs/scan to 3.4% with the highest sensitivity of
98.6%.
(a)
(b)
Fig 4.7: FROC analysis (a) FROC curve shows false positive reduction by each classifier,
(b) FROC curve illustrates the sensitivity comparison of each classifier at 3.4 FPs/scan
111
In Fig 4.7 (b), another FROC curve is given, which illustrates the comparison of
classifiers‘ sensitivity at 3.4 FPs/scan. It depicts that SVM-ensemble has the highest
sensitivity, i.e., 98.6% and SVM-linear has the lowest. SVM-RBF has better
sensitivity than SVM-polynomial at 3.4 FPs/scan. It is evident from FROC curves that
minimum FPs/scan with SVM-ensemble is reduced to 1 but at the cost of sensitivity
degradation with 92.1%.
4.4.2 Comparative Analysis
For estimating the significance of the proposed method in relation to reported
techniques in the literature, a comparison of datasets and their results is presented in
Table 4.17 and Table 4.18, respectively.
Table 4.17: Comparison of datasets used in existing and proposed systems
Methodology Year Dataset Scans Nodules Nodule Size
Dehmeshki, et al. [49] 2007 Clinical 70 179 3-20 mm
Ye, et al. [57] 2009 Clinical 108 220 3-20 mm
Tan, et al. [48] 2011 LIDC 125 80 3-30 mm
Riccardi, et al. [56] 2011 LIDC 154 117 3-30 mm
Cascio, et al. [94] 2012 LIDC 84 148 3-30 mm
Teramoto and Fujita [97] 2013 LIDC 84 103 5-20 mm
Choi and Choi [54] 2014 LIDC 84 148 3-30 mm
Kuruvilla and Gunavathi [103] 2014 Clinical 155 110 3-30 mm
Han, et al. [61] 2015 LIDC-IDRI 205 490 3-30 mm
Nibali, et al. [118] 2017 LIDC-IDRI - - 3-30 mm
Jaffar, et al. [124] 2018 LIDC 84 148 3-30mm
Teramoto and Fujita [125] 2018 Clinical 100 186 -
Zhang, et al. [123] 2018 LIDC-IDRI 71 168 3-30mm
Shaukat, et al. [131] 2019 LIDC 84 148 3-30mm
Huidrom, et al. [130] 2019 LIDC-IDRI 300 - 3-30mm
Proposed Method LIDC-IDRI 250 567 3-30 mm
112
Ye, et al. [57] used clinical data of 108 scans containing 220 nodules of size 3-20mm
with a slice thickness of 0.5-2mm. Table 4.17 illustrates that some methods used 84
CT scans of LIDC dataset consisting of 148 nodules marked by any of the radiologists
[54, 94, 124, 131]. Dehmeshki et al. [49] made use of 70 CT scans of a clinical dataset
containing 179 nodules of size 3-20mm with a slice thickness of 0.5-1.25mm.
Kuruvilla and Gunavathi [103] utilized a clinical dataset of 155 scans with a slice
thickness of 0.75-1.25mm, including 110 nodules. In addition, they have also used
some scans of LIDC dataset without LIDC annotations. Han, et al. [61] used 205 CT
scans of LIDC-IDRI dataset containing 490 nodules marked by any of the
radiologists. The nodules included in their study were of 3-30mm size. Teramoto and
Fujita [97] made use of 84 CT scans of LIDC dataset with 103 nodules of size 5-
20mm marked by any of the radiologists. Nibali, et al. [118] utilized LIDC-IDRI
dataset for experimentations, but details are not provided. Tan, et al. [48] made use of
125 CT scans of LIDC dataset containing 80 nodules of size 3-30mm marked by all
the four radiologists. Riccardi, et al. [56] used 154 CT scans of LIDC dataset
consisting of 117 nodules with size 3-30mm marked by all the four radiologists.
Huidrom, et al. [130] used 300 scans, but nodule information is not provided. Zhang,
et al. [123] used a smaller subset of LIDC-IDRI dataset of 71 scans containing 168
nodules. The proposed method is evaluated over 250 scans of LIDC-IDRI dataset
containing 567 nodules with size 3-30mm annotated by all four radiologists.
The statistics in Table 4.17 highlight the fact that the proposed method is tested over a
more significant number of scans and nodules as compared to other research works
reported in the literature. Table 4.18 compares the results of the proposed and existing
methods. Kuruvilla and Gunavathi [103] produced 91.4% sensitivity but with a very
high FPs/scan of 30. Moreover, they did not provide specificity. Choi and Choi [54]
claimed 97.5% sensitivity as well as specificity with 6.76 FPs/scan. Ye, et al. [57]
produced 8.2 FPs/scan with 90.2% sensitivity. Cascio et.al [94] claimed 88%
sensitivity and 97% accuracy with 6.1 FPs/scan. Teramoto and Fujita [97] achieved a
low FPs/scan of 4.2 but at the cost of lower accuracy of 80%. Han, et al. [61] claimed
a low FPs/scan of 4.14, but their sensitivity was low as 89.2%. Nibali, et al. [118]
showed 89.9%, 91.07% and 88.64% accuracy, sensitivity, and specificity,
respectively.
113
Table 4.18: Comparison of results with existing methods
Methodology Year Accuracy(%) Sensitivity(%) Specificity(%) FPs/Scan
Ye, et al. [57] 2009 - 90.2 - 8.2
Tan, et al. [48] 2011 - 87.5 - 4.0
Riccardi, et al. [56] 2011 - 71.0 - 6.5
Cascio, et al. [94] 2012 97.0 88.0 - 6.1
Teramoto and Fujita [97] 2013 80.0 - - 4.2
Choi and Choi [54] 2014 99.0 97.5 97.5 6.76
Kuruvilla and Gunavathi [103] 2014 - 91.4 - 30
Han, et al. [61] 2015 95.88 89.2 - 4.14
Nibali, et al. [118] 2017 89.9 91.07 88.64 -
Jaffar, et al. [124] 2018 98.7 97.5 98.18 -
Teramoto and Fujita [125] 2018 - 83.0 - 5.0
Zhang, et al. [123] 2018 - 89.3 - 2.1
Shaukat, et al. [131] 2019 93.7 95.5 94.28 5.72
Huidrom, et al. [130] 2019 93.23 93.26 93.2 -
Proposed Method 99.0 98.6 98.2 3.4
Tan, et al. [48] produced 87.5% sensitivity with 4.0 FPs/scan and Riccardi, et al. [56]
claimed 6.5 FPs/scan with a low sensitivity of 71.0%. Huidrom, et al. [130] produced
93.26% sensitivity, but number of FPs is not reported. Zhang, et al. [123] claimed
better FPs/scan than the proposed method. However, their sensitivity is very low in
comparison to the proposed method. Besides, they have used a smaller size dataset for
experimentation. By concluding the whole discussion, it is evident that the proposed
method has shown better results, i.e. 99.0% accuracy, 98.6% sensitivity and 98.2%
specificity with a 3.4 FPs/scan which is a clear proof of the effectiveness.
4.5 Method IV: Lung Nodule Detection and Classification based on
Geometric Fit in Parametric Form and Deep Learning
The details of the dataset, data distribution, results of segmentation, initial candidate
detection and classification are discussed in this section. MATLAB 2016b is used as
114
an implementation tool, on Intel Xeon 3.5 GHz with 16 GB RAM, Windows Server
2012 R2 Standard.
4.5.1 Dataset Selection
The performance evaluation is conducted over a publicly available dataset LIDC-
IDRI [34]. It is the largest dataset of lung CT images, which has 1018 scans of 1010
patients. Each scan contains 100 to 400 slices with slice thickness 0.6mm to 5mm and
dimensions 512x512. The nodules in the dataset are of size 3mm to 30mm in diameter
[35]. There are 121 scans which have slice thickness ≥3mm. These scans are not
suitable to use in computer-based diagnosis systems [193]. In addition to that nine
scans have inconsistent slice spacing and not suitable for automated diagnosis
systems. As a result, 888 scans are left. Four experienced radiologists examine all the
scans for nodule annotation. These annotations are given in XML format, along with
each scan. Total 777 nodules are used for experimentation, which are marked by all
the radiologists.
4.5.2 Segmentation Results
Accurate results of segmentation affect the performance of successor steps. The initial
values of FODPSO parameters are given in Table 4.19.
Table 4.19: FODPSO initialization parameters
Parameter Initial Value
Total number of iterations ( ) 100
Number of swarms ( ) 4
Minimum number of swarms ( ) 2
Number of particles in each swarm ( ) 20
Minimum number of particles in each swarm ( ) 10
Maximum number of particles in each swarm ( ) 30
Maximum number of swarms ( ) 6
Fractional coefficient ( ) 0.6
Global weight controller ( ) 1.2
Local weight controller ( ) 0.8
115
The position of each particle is initialized within the gray intensity levels from to
. The results of the segmentation using FODPSO are compared with the existing
methods. Three quantitative measures are used, including Jaccard measure [194],
probabilistic random index (PRI) [195] and variation of information (VoI) [196], as
performance measures. The Jaccard similarity coefficient compares the similarity
between the segmented image ( ) and the reference ground truth image ( ). The
mathematical form of Jaccard is given in Eq. 4.1.
(4.1)
The second parameter, probabilistic random index (PRI) is used to check the
consistency of the segmented image with reference to the actual image. It checks the
degree of closeness of intensities of a pair of pixels in the segmented and original
image. The PRI value ranges from 0 to 1, where 1 yields an ideal segmentation. The
PRI value can be calculated by Eq. 4.2.
( )∑ ∑ ( ( )
( ))
(4.2)
where is a discriminant function that determines whether the pixel in has the
same label with the corresponding pixel in and is the number of overlapping
regions. In information theory, the Variation of Information (VoI) also known as
shared information, quantifies the degree of mutual information in the segmented
image and the ground truth image. The VoI can be computed by Eq. 4.3
(4.3)
where H(Is) and H(Ir) are the entropies calculated by the Eq. 4.4 and Eq. 4.5 given
below.
∑
(4.4)
116
∑
(4.5)
M(Is, Ir) represents the mutual information and is computed by Eq. 4.6.
∑∑
(4.6)
The value of VoI ranges from 0 to ∞, where 0 represents perfect segmentation. The
segmentation results of FODPSO are compared with the well-known methods like
fuzzy entropy based optimal threshold, Otsu method and use of superpixel for
segmentation. Table 4.20 gives a brief comparison of the segmentation results, which
depicts that proposed FODPSO based segmentation has significantly improved the
results.
Table 4.20: Comparison of segmentation results
Method Year Measure Without Nodules Pleural Nodules
Jaffar, et al. [197] 2010 VoI 1.5428 3.893
PRI 0.9527 0.8729
Jaccard 0.9254 0.8723
Choi and Choi [96] 2012 VoI 1.779 3.825
PRI 0.9581 0.8556
Jaccard 0.9375 0.8535
Liao, et al. [198] 2016 VoI 1.3815 2.2052
PRI 0.9624 0.8926
Jaccard 0.9413 0.8821
Jaffar, et al. [124] 2018 VoI 1.1152 1.9748
PRI 0.9695 0.9142
Jaccard 0.9587 0.9032
Proposed Method
VoI 1.013 1.9712
PRI 0.9703 0.9201
Jaccard 0.9621 0.9093
117
(a)
(b)
Fig 4.8: 3D lung volume (a) left view and (b) right view
118
(a)
(b)
Fig 4.9: Extracted 3D ROI with nodules and vessels (a) normal view of the
complete lung (b) a portion containing juxta-vascular nodule
119
The 3D volume is shown in Fig 4.8, which also indicates a juxta-pleural nodule. It
illustrates the accuracy of the segmentation. Furthermore, Fig 4.9 shows a 3D ROI of
the segmented lung region, including vessels and nodules. The 3D volumes are shown
in Fig 4.8 and Fig 4.9 consists of 250 slices with 512x512 dimensions on z, x, and y-
axis, respectively. Fig 4.9 (b) shows a portion of ROI shown in Fig 4.9 (a), which
highlights some juxta-vascular nodules.
4.5.3 Initial Candidate Detection Results
After the lung volume extraction, the nodule candidate detection is performed. The
initial candidates are extracted on the basis of geometric fit in parametric form.
Afterwards, a geometric rule-based refinement is performed on the nodule candidate
set. In the initial stage, large numbers of nodule candidates are generated. In this
phase, all actual nodules are successfully included in the candidate set with 100%
sensitivity with 205.4 FPs/scan. In the refinement phase of candidate detection, the
number of false positives is reduced to 82584 with 93 FPs/scan. However, during the
refinement phase, 13 actual nodules are also missed, and sensitivity degrades to
98.33% with 1.67% false negative rate. The number of false positives is further
reduced in the classification phase.
4.5.4 Classification Results
After segmentation, nodule candidates are extracted, including 764 actual nodules and
the remaining are non-nodules. It includes juxta-vascular, juxta-pleural and isolated
nodules of size ranging from 3mm to 30mm. The classification is performed with
deep learning. The classifiers are trained and tested over holdout cross validation with
three different data distributions 80-20, 70-30 and 50-50. The experiments with each
data distribution are repeated 10 times and mean values are computed. The best
results are achieved with 70-30 data distribution, where 70-30 represents 70% training
and 30% test data, respectively. These results are presented in Table 4.21 and Table
4.22. The first phase of this learning network is a stacked autoencoder followed by
softmax classifier. The stacked autoencoder is used for feature optimization, which
improves the performance of the classifier. In the feature optimization step, two level
autoencoder is used. The weight regularization is initialized to 0.001, sparsity
regularization to 4, and sparsity proportion to 0.05. Fig 4.10 shows the working
structure of a simple autoencoder. It takes a 44 size feature vector and encodes it to a
120
reduced set of 10 feature vector. In the second step, it decodes back to a 44 size
feature vector. However, in the case of stacked autoencoder, instead of decoding the
encoding layer pass the output to the next encoding layer. As a result, a hierarchy of
encoding is formed, which results in the lower dimensional feature vector. The size 10
is selected empirically. Experimentation is performed with different sizes of hidden
layers ranging from 5 to 14, and it is found that the best classification is at value 10.
This feature vector is passed to the softmax to distinguish nodules from other objects.
Fig 4.10: The first layer of autoencoder
In Fig 4.11 two autoencoders are stacked, which reduces the 44-dimensional feature
vector to a 10-dimensional feature vector. This feature reduction has improved the
performance of the classifier in terms of computation time and accuracy.
Fig 4.11: Stacked autoencoder and softmax for feature reduction and classification
Table 4.21 shows the classification results with and without feature reduction. It is
clear that when the classification is performed by softmax without feature
optimization, the results are poor. The results with one level autoencoder are
significantly improved, but the overall performance is very poor. However, when a
two level autoencoder is used, it significantly improves the classification
performance.
121
Table 4.21: Results with different variations of deep net
Classifier Accuracy(%) Sensitivity(%) Specificity(%) FPs/Scan
Two Level SA+softmax 96.9 95.6 97.0 2.8
One encoder +softmax 90.3 89.8 84.6 11.7
Without autoencoder 75.9 68.6 49.8 45.2
The results of the deep net classification are compared with the state of the art
classifiers, including SVM [199], k-NN [200], Naïve Bayes [201] and Decision Tree
[202] as illustrated in Table 4.22. The proposed deep net has outperformed the state of
the art classifiers in all the metrics. Fig 4.12 shows free-response receiver operating
characteristic (FROC) curves, which gives a brief comparison of sensitivity and
FPs/scan produced by each classifier. Fig 4.12(a) illustrates the FPs produced by each
classifier.
SVM produced 3.4 FPs/scan with 92.6% sensitivity, Naïve Bayes produced 90.8%
sensitivity with 6.4 FPs/scan, k-NN produced 85.3% sensitivity with 8.8 FPs/scan and
decision tree produced 89.2% sensitivity with 6.9 FPs/scan, whereas the proposed
method achieved 95.6% sensitivity with very low FPs of 2.8 per scan. Fig 4.12 (b)
shows another FROC curve providing a comparison of sensitivity produced by each
classifier at 2.8 FPs/scan. It shows that there is a wide difference of sensitivity
produced by the proposed method and other state of the art classifiers like SVM has
86.9%, Naïve Bayes has 82.0%, k-NN has 75.3%, and decision tree has 79.0%. These
results are clear evidence of the significance of the proposed method.
Table 4.22: Comparison of classification results
Classifier Accuracy(%) Sensitivity(%) Specificity(%) FPs/Scan
SVM 96.3 92.6 96.4 3.4
Naïve Bayes 91.2 90.8 91.6 6.4
k-NN 84.2 85.3 83.8 8.8
Decision Tree 89.7 89.2 89.6 6.9
SA +softmax 96.9 95.6 97.0 2.8
122
(a)
(b)
Fig 4.12: FROC analysis (a) FROC curve showing the performance of each classifier, (b)
FROC curve comparing the performance of classifiers at 2.8 FPs/scan
123
4.5.5 Comparative Analysis
Table 4.23 provides a brief comparison of the proposed and reported methods in the
literature based on common parameters. It is important to mention that some methods
use clinical data and others have used LIDC and its extended version LIDC-IDRI.
However, researchers have used different subsets of these datasets with variations in
nodule sizes and annotations. Kuruvilla and Gunavathi [103] produced 91.4%
sensitivity and 30 FPs/scan on a dataset of 155 scans containing 110 nodules.
Riccardi, et al. [56] worked on 154 LIDC scans containing 117 nodules, but their
results are not promising, i.e. 6.5 FP/scans with 71.0%.sensitivity. Brown, et al. [107]
achieved 2.0 FPs/scan, but these results are on a small dataset of 108 CT scans
containing 68 nodules. In addition, they have only considered large nodules of size
4mm ≥, and sensitivity is also very low. Nibali, et al. [118] produced 89.9% accuracy,
91.1% sensitivity, and 88.6% specificity respectively, but they have not provided the
details about the size of dataset and number of FPs. Cascio et.al [94] produced 88.0%
sensitivity, 97.0% accuracy and 6.1 FPs/scan on 84 CT scans containing 148 nodules,
from LIDC, of size 3-30mm with consensus of all four radiologists. Taşcı and Uğur
[55] targeted only juxta-pleural nodules and evaluated performance on 24 scans
containing 124 nodules produced 95.88% accuracy. The evaluation was performed
over LIDC dataset. Teramoto and Fujita [97] used 84 scans of LIDC dataset and
claimed 4.2 FPs/scan and 80% accuracy. They have not considered the smaller
nodules <5mm. In another method, Teramoto and Fujita [125] used clinical data and
produced 83% sensitivity and 5.0 FPs/scan. Tan, et al. [48] produced 4.0 FPs/scan
and 87.5% sensitivity over a subset of LIDC dataset of 125 CT scans containing 80
nodules of 3-30mm size, annotated by all 4 radiologists. Messay, et al. [89] reported
3.0 FPs/scan, 82.7% sensitivity and 80.4% accuracy. Total 143 nodules from 84 CT
scans of LIDC dataset were used for the evaluation. Han, et al. [61] evaluated their
method over a subset of LIDC-IDRI dataset of 205 CT scans containing 490 nodules
with agreement level one and claimed overall 82.7% sensitivity with 4.0 FPs/scan.
Dou, et al. [119] used 888 CT scans containing 1186 nodules. They achieved 90.7%
sensitivity and 4.0 FPs/scan. Ali, et al. [127] used 888 scans containing 1148 nodules
to evaluate their proposed CNN, but their results are not promising. Xie, et al. [132]
achieved 86.42% sensitivity during the candidate detection process. In classification,
it is reduced to 83.4%, 83.2%, and 73.4% with 8, 4, and 0.125 FPs/scan respectively.
124
Tab
le 4
.23
: Com
para
tive a
naly
sis in term
s of d
ata
set an
d resu
lts
Meth
od
olo
gy
Y
ear
Sca
ns
No
du
les N
od
ule S
ize
Da
taset
Acc
ura
cy(%
) S
ensitiv
ity(%
) S
pec
ificity(%
) F
Ps/S
can
Messa
y, et al. [8
9]
20
10
84
14
3
3-3
0m
m
LID
C
80
.4
82
.7
- 3
.0
Tan
, et al. [48
] 2
01
1
12
5
80
3-3
0m
m
LID
C
- 8
7.5
-
4.0
Riccard
i, et al. [56
] 2
01
1
15
4
11
7
3-3
0m
m
LID
C
- 7
1.0
-
6.5
Cascio
, et al. [94
] 2
01
2
84
14
8
3-3
0m
m
LID
C
97
.0
88
.0
- 6
.1
Teram
oto
and
Fujita [9
7]
20
13
84
10
3
5-2
0m
m
LID
C
80
.0
- -
4.2
Kuru
villa a
nd
Gu
navath
i [10
3]
20
14
15
5
11
0
3-3
0m
m
Clin
ical -
91
.4
- 3
0.0
Bro
wn, et al. [1
07
] 2
01
4
10
8
68
4-3
0m
m
LID
C-ID
RI
- 7
5.0
-
2.0
Han
, et al. [61
] 2
01
5
20
5
49
0
3-3
0m
m
LID
C-ID
RI
95
.88
82
.7
- 4
.0
Taşcı an
d U
ğur [5
5]
20
15
24
12
4
- L
IDC
9
5.8
8
- -
-
Nib
ali, et al. [11
8]
20
17
- -
3-3
0m
m
LID
C-ID
RI
89
.9
91
.1
88
.6
-
Do
u, et al. [1
19
] 2
01
7
88
8
11
86
3-3
0m
m
LID
C-ID
RI
- 9
0.7
-
4.0
Teram
oto
and
Fujita [1
25
] 2
01
8
10
0
18
6
3-3
0m
m
Clin
ical -
83
.0
- 5
.0
Ali, et al. [1
27
] 2
01
8
88
8
11
48
3-3
0m
m
LID
C-ID
RI
64
.4
58
.9
55
.3
-
Zhan
g, et al. [1
23
] 2
01
8
71
16
8
3-3
0m
m
LID
C-ID
RI
93
.6
89
.3
- 2
.1
Shaukat, et al. [1
31
] 2
01
9
84
14
8
3-3
0m
m
LID
C
93
.7
95
.5
94
.28
5.7
2
Huid
rom
, et al. [13
0]
20
19
30
0
- 3
-30
mm
L
IDC
-IDR
I 9
3.2
3
93
.26
93
.2
-
Xie, et al. [1
32]
20
19
88
8
- 3
-30
mm
L
IDC
-IDR
I -
83
.4
- 8
.0
Pro
po
sed M
etho
d
- 8
88
77
7
3-3
0m
m
LID
C-ID
RI
96
.9
95
.6
97
.0
2.8
124
125
Huidrom, et al. [130] used 300 CT scans of LIDC-IDRI dataset and produced 93.26%
sensitivity, but details about the number of nodules and FPs are not reported. Shaukat,
et al. [131] achieved 95.5% sensitivity with 5.72 FPs/scan. Zhang, et al. [123] claimed
a low FPs/scan of 2.1, but their sensitivity is significantly low, i.e., 89.3%.
Furthermore, they have achieved these results on a smaller subset of LIDC-IDRI
dataset contacting only 71 scans. Brown, et al. [107] claimed better FPs/scan than the
proposed method. However, they have used a smaller dataset and nodules greater than
4mm whereas, in this study, nodules greater than 3mm are considered for evaluation.
These facts depict the strength of the proposed method.
The results have been verified by clinical experts who have shown confidence that the
proposed methods work on clinical data and will facilitate the radiologists during the
diagnosis process by providing a second opinion, which will save the time of
radiologists.
4.6 Summary
This chapter presents the experiments and results of the proposed methods and their
comparison to the methods reported in the literature. The experimentation is
performed with the most popular and authentic publicly available datasets, including
LIDC and LIDC-IDRI, the latter being the largest. The first and second methods are
evaluated on the LIDC dataset and reduced the number of false positives to 3.7 and
3.3 per scan, respectively. The third and fourth methods are evaluated on the LIDC-
IDRI and reduced the number of false positives to 3.4 and 2.8 per scan, respectively.
The experimental results, therefore, show that there is a significant improvement in
results. Each method has reduced the number of false positives with high sensitivity.
126
Chapter 5
Conclusion and Future Work
127
5.1 Conclusion
Precise and intelligent detection and classification of pulmonary nodules is an arduous
task in the field of medical image analysis. The complex lung structure makes this
problem even more difficult. A radiologist has to examine a large number of CT
images during the diagnosis process. Automated diagnosis systems provide support to
the radiologists to identify the disease in less amount of time by providing the second
opinion. However, there is a need to handle different types of nodules more carefully.
Precise segmentation of nodules helps to overcome these issues. Moreover, relevant
features and robust classifier(s) also assert an enormous impact on the performance of
a diagnosis method.
This thesis has highlighted the issues in the existing approaches on different phases of
the diagnosis process, including segmentation, feature extraction and classification.
We proposed novel methods to address these issues. Our research provides precise
detection and classification of different nodules, including isolated, juxta-pleural and
juxta-vascular nodules. Four methods are proposed in this thesis on the basis of
problems identified from the literature.
In the first method, a novel diagnosis method is proposed based on polygon
approximation and hybrid features. During this study, dynamic optimal thresholding
is computed to extract the lung region. Polygon approximation is proposed to
eliminate the vessels and extraction of nodule candidates from ROI. CLAHE is used
to enhance the nodule candidates. A hybrid feature vector based on geometric,
intensity and HOG features is constructed, and SVM is applied using linear, RBF and
polynomial kernels to distinguish nodules and non-nodules. Experiments show that
the RBF kernel has outperformed the linear and polynomial kernels regarding the
accuracy, sensitivity, and specificity. Experimentation is performed on a publicly
available dataset LIDC, and achieved an accuracy of 98.8%, 97.8% sensitivity with
3.7 FPs per scan.
In method II, a novel nodule candidate detection method is presented. Furthermore,
for better classification of these nodule candidates, seven different feature vectors are
created using a different combination of individual features. Four different classifiers
are trained and tested to identify actual nodules. A number of experiments are
performed with different distributions of data selected from LIDC dataset. It is found
128
that the performance of k-NN and Naïve Bayes is not good. However, SVM has
produced better results than these two. The best results are achieved with AdaBoost
classifier showing an accuracy of 99.2% with sensitivity 98.3%, specificity 98.0%,
and 3.3 FPs/scan. These results are better than the existing techniques in the literature.
In the third method, a more effective 3D segmentation method for lung region and
nodule candidates is proposed. A critical issue, with the existing nodule diagnosis
methods, is the detection of small, boundary attached and vessel attached nodules. If a
detection method misses any nodule, it leads to an inaccurate diagnosis, which might
be life-threatening. To handle such problems, a nodule segmentation method from 3D
CT scans is presented. The first step of the proposed method is based on a hybrid
multistep segmentation technique to extract lung ROI and nodule candidates. SVM-
ensemble is used to distinguish nodules from the set of selected candidates. The
classification is based on a GTFD feature vector encapsulating texture and geometric
features. The proposed method is tested LIDC-IDRI, which is an extended version of
LIDC dataset. The presented method achieved 99.0% accuracy with 98.6% sensitivity
and 98.2% specificity. The number of FPs/scan is also low as 3.4/scan.
The primary focus of the fourth method is the reduction of false positives by
preserving sensitivity. Initial segmentation of lung ROI is performed on the basis of
FODPSO. In the second step, a novel nodule candidate detection method based on
geometric fit in parametric form is proposed, which distinguishes the nodules and
vessels precisely. It impacts the overall performance of the detection method.
Furthermore, a hybrid GTFD is created for better representation of the candidate
nodules. Finally, a stacked autoencoder and softmax algorithm are applied for feature
optimization and classification of nodules. The dataset used in this study is collected
from LIDC-IDRI dataset with nodules of size ≥3mm. The proposed method
significantly improves the results and achieves a low false positive rate as 2.8/scan
with 96.9% accuracy, 95.6% sensitivity, and 97.0% specificity. The comparative
analysis depicts that the proposed methods have significantly reduced the FPs/scan
with high sensitivity. The results have also been verified by the clinical experts.
The comparison of these methods can help the users to select the more appropriate
method to use. The first method performs 2D segmentation of slices. This
shortcoming is eliminated by the second method by performing 3D image analysis
during segmentation and in feature extraction phases. Juxta-vascular nodules are
129
difficult to segment due to their attachment with vessels. In the third method, a robust
multistage segmentation model is proposed to detect different types of nodules
precisely. Instead of slice level segmentation, a novel scan level 3D segmentation is
performed to extract juxta-vascular nodules. It is an important aspect of this method,
which improves the detection process. It outperforms the existing methods. The
SVM-ensemble classifier is implemented based on three heterogeneous kernels. These
improvements impacted the performance of diagnosis methods and outperformed the
existing methods.
In the fourth method, instead of improving one phase of the diagnosis process, we
have improved every phase of the diagnosis process. Furthermore, deep learning is
applied for feature reduction and classification, which strengthens the diagnosis
method to process such a large number of images. It results in the evaluation of the
diagnosis method on 888 CT scans with more than 10 million images. Method II is
better than Method I as it improves the nodule candidate detection process by refining
the initial candidate set. It reduces the number of FPs. Method III and IV are better
than method II due to the better segmentation of juxta-vascular nodules. Furthermore,
in method IV deep learning base nodule classification is performed, which has the
strength to process big data. It is a hot topic in the current era of research. Concluding
the discussion, we can say that all four methods have their strengths. However,
Methods II, III and IV are significantly stronger than the first one. If users have big
data of patients and high processing machines then Method IV will be the best choice
as it also has the strength of deep learning. Combining multiple methods may be a
good approach to achieve good performance. However, it requires high processing
machines. Furthermore, each diagnosis method produces false positives. A
combination of multiple methods may increase false positives. This can be another
area of research to investigate in future.
The investigations conducted in this research lead to the following conclusions:
A robust optimization algorithm improves the computation of threshold
selection, which leads to precise lung volume extraction and better detection
of juxta-pleural nodules from a CT scan.
A multistage segmentation method can extract different types of nodules more
precisely, which impacts the overall performance of a diagnosis method.
130
Scan level segmentation of juxta-vascular nodules improves the sensitivity and
also reduces the false positives as compared to slice level segmentation.
The geometric features, along with texture features, improve the performance
of the diagnosis system. However, the geometric features are more significant
to detect and classify nodules by investigating the shape of the objects within
the CT scan.
Ensemble classifiers and deep neural networks have improved classification
performance. Deep learning also provides the strength to a diagnosis method
to deal with large number of scans.
5.2 Future Work
The proposed methods are tested and evaluated for isolated, juxta-pleural and
juxta-vascular nodules. However, in the future, these methods can be extended
to other types of lung diseases by identifying disorders in the tissue patterns
such as endobronchial nodules, carcinomas presenting as irregular wall
thickening of lung bullae, hilar lesions. The main issue with these diseases is
the availability of a small data, which results in poor training of the classifiers.
Deep learning is a new hot area of research. In this thesis, deep learning is
applied, including autoencoder for feature reduction and softmax for nodule
classification. In the future, Convolutional Neural Network (CNN) can be used
for direct feature extraction and classification. However, there is a big issue
with CNN that it requires high computation during the training process, which
requires high-performance machines and Graphics Processing Unit (GPU).
Current repositories of lung CT scans contain one scan for each patient. The
diagnosis process can be improved by examining multiple scans of a patient
taken on different time spans. The change in the lung can be observed
precisely by comparing two different scans of a patient.
131
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List of Publications
1. Syed Muhammad Naqi, Muhammad Sharif, Ikram Ullah Lali (2019) "A 3D
nodule candidate detection method supported by hybrid features to reduce
false positives in lung nodule detection," Multimedia Tools and Applications,
vol. 78(18), pp. 26287-26311, (Impact Factor: 2.1)
2. Syed Muhammad Naqi, Muhammad Sharif, Arfan Jaffar (2018) ―Lung
Nodule Detection and Classification based on Geometric Fit in Parametric
Form and Deep Learning,‖ Neural Computing and Applications.
https://doi.org/10.1007/s00521-018-3773-x (Impact Factor: 4.66), published
online.
3. Syed Muhammad Naqi, Muhammad Sharif, Mussarat Yasmin, (2018)
―Multistage Segmentation Model and SVM-Ensemble for Precise Lung
Nodule Detection,‖ International Journal of Computer Assisted Radiology and
Surgery, 13(7) 1083-1095 (Impact Factor: 2.15)
4. Syed Muhammad Naqi, Muhammad Sharif, Mussarat Yasmin, Steven
Lawrence Fernandes (2018) ―Lung Nodule Detection Using Polygon
Approximation and Hybrid Features from Lung CT Images,‖ Current Medical
Imaging Reviews, 14(1) 108-117 (Impact Factor: 0.53)
5. Syed Muhammad Naqi and Muhammad Sharif (2017) "Recent
Developments in Computer Aided Diagnosis for Lung Nodule Detection from
CT images: A Review," Current Medical Imaging Reviews, 13(1) 3-19.
(Impact Factor: 0.53)
156
Appendix:
WHO Cancer Statistics
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