chapter 3 segmentation of lung region using fuzzy possibilistic c...
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CHAPTER 3
SEGMENTATION OF LUNG REGION USING FUZZY
POSSIBILISTIC C-MEANS (FPCM)
3.1 INTRODUCTION
Cancer is a disease in which abnormal cells of the body divide very
fast, and generate excessive tissue that forms a tumor. Cancer cells are
capable of spreading to other parts of the body through the blood and lymph
systems. When the uncontrolled cell growth occurs in one or both lungs, it is
said to be Lung Cancer. Besides, developing into a healthy, normal lung
tissue, these abnormal cells continue dividing and form lumps or masses of
tissue called tumors. The main function of the lung which is to carry the
bloodstream with oxygen to the entire body is disturbed by these tumors.
There are many types of cancers.
3.1.1 Types of Lung Cancer
Cancers that begin in the lungs are divided into two major types,
non-small cell lung cancer and small cell lung cancer, depending on how the
cells look under a microscope. Each type of lung cancer grows and spreads in
different ways and is treated differently.
3.1.2 Small cell lung cancer (SCLC)
This is usually believed to be a systemic disease at the time of
diagnosis and thus surgery plays no part in the management of this disease.
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3.1.3 SCLC staging
Limited disease: It is limited to one hemi thorax that can be
included in a reasonable field of thoracic radiation therapy.
Extensive disease: It is beyond one hemi thorax or that cannot be
included in a reasonable field of thoracic radiation therapy.
3.1.4 Non-small cell lung cancer (NSCLC)
NSCLC aggravates heavily later in its course than SCLC and
consequently surgery is the best chance of cure. Patients those who are
considered for surgical treatment must be carefully staged to determine tumor
resectability. Positron Emission Tomography (PET) will help to assess modal
in involvement. But, only 15% of patients are appropriate for resection at
diagnosis. The patient must also be carefully assessed pre-operatively to
assure fitness for surgery.
Small-cell lung cancer differs from non-small-cell lung cancer in
the following ways:
SCLC grows quickly.
SCLC spreads quickly.
SCLC responds well to chemotherapy and radiation therapy
SCLC is often associated with distinct paraneoplastic
syndromes
Lung cancer is one of the most dangerous cancers in the world,
with the smallest survival rate after diagnosis, with a gradual increase in the
mortality rate every year. Figure 3.1 shows an example of cancerous lung
image. The survival probability from lung cancer is indirectly proportional to
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its growth at its detection time. The chances of successful treatment are
possible only if the disease is detected at the earlier stage. An estimated result
shows that 85% of lung cancer cases in males and 75% in females are caused
by cigarette smoking (Singapore Cancer Society). The overall survival rate
for all types of cancer is 63%. Though surgery, radiation therapy, and
chemotherapy have been used in the treatment of lung cancer, the five-year
survival rate for all stages combined is only 14%. This has not changed in the
past three decades (American Cancer Society, 2005).
Figure 3.1 An example of cancerous lung image
Several thin-sectional CT images are produced in clinic for each
patient and are estimated by a radiologist by looking at each image in the
axial mode. Most of the images will be very tough to interpret and consume a
lot of time that can cause high false-negative rates for detecting small lung
nodules, and thus potentially miss a cancer. The fundamental idea of
designing a CAD system is to make a machine algorithm that acts as a support
to the radiologist and points out locations of doubtful objects, so that the
overall sensitive rate is raised.
CAD system must achieve following needs
improving the quality and accuracy of diagnosis,
increasing success in therapy by early detection of cancer,
avoiding unnecessary biopsies,
Reducing radiologist interpretation time (Wiemker et al 2005).
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A CAD system for early detection of lung cancer by analyzing the
images of CT is proposed in this chapter. Fuzzy Possibilistic C-Means
(FPCM) is used for clustering the cancerous and non-cancerous nodules.
3.2 NEURAL NETWORKS IN CANCER DETECTION
Various neural network techniques have been employed in the
cancer detection approaches. Recently, ANNs is a main research area in
health care modeling and it is believed that they will receive extensive
application to biomedical systems in the coming years (Lin et al 2002).
Neural networks learn by examples and so the ways to recognize the disease
is not needed. A set of examples (patterns) is only needed that are
representative of all the variations of the particular disease. A high accuracy
level in the disease recognition is obtained by carefully choosing the patterns.
3.2.1 Artificial neural networks (ANN)
These are basic models of the biological nervous system and are
inspired from the kind of computing executed by a human brain. An ANN
is an extremely parallel distributed processing system made up of highly
interconnected neural computing elements (neurons) that offer the ability
to learn and thus acquire knowledge and make it available for use
(Rajasekaran et al 2003). The data obtained by electrical impedance
spectroscopy has a strong relation with soft computing in identifying
cancerous area from the normal area. So ANN which is an information
processing system can be used as an appropriate tool for cancer detection.
Certain performance characteristics of ANN are common with
biological neural networks. An ANN contains some nodes which are
connected through weights. Each node obtains data from previous nodes,
attaches it and passes data via a nonlinear function, and then propagates data
to succeeding nodes. Two phases are involved in the evaluation of ANN
performance:
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Training phase
Test phase
The input patterns are offered to the ANN and weights are adjusted
and fixed to learn these patterns in the training phase. ANN certainly learns
input patterns in learning phase. On the other hand, the patterns which are not
used in training phase are presented to the ANN in the test phase and the
ANN’s outputs are used to estimate its performance (Alizadeh et al 2008). If
the performance of ANN is satisfactory, it can be used in its own specific
application.
ANNs are classified into two categories like supervised and
unsupervised learning. ANN is supervised only if the outputs of the input
patterns used in the training phase of the ANN are available through a
particular experiment, and otherwise it is unsupervised. Supervised ANNs can
also be categorized into two groups namely error-based and prototype-based.
The main aim of error-based network is to reduce the cost function which is
defined on the basis of error between the desired output and the network
output. The main aim of the prototype-based network is to reduce the distance
between the inputs patterns and the prototypes which are assigned to each
cluster.
The Multilayer Perceptron (MLP) and Radial Basis Function (RBF)
networks are the examples of error-based networks and the Linear Vector
Quantization (LVQ) is the example for prototype based network. The MLP
training is based on the minimization of a suitable cost function, and is called
the back propagation algorithm. The first version of this algorithm based on
the gradient descent technique was proposed by Werbos (1974) and Parker
(1982).
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The fundamental construction of a Radial Basis Function (RBF)
network constitutes three layers with entirely different roles. The inputs layer
consists of source nodes that connect the network to its environment. A
nonlinear transformation from the input space to the hidden space is applied
in the second layer; in most applications the hidden space is of high
dimensionality. The output layer is linear, providing the response of the
network to the activation pattern applied to the input layer (Vakil Baghmisheh
et al 2004, Haykin 2006).
Linear Vector Quantization (LVQ) was introduced by Linde et al
(1980) and Gray (1984). It was initially used for image data compression and
later was adapted by Kohonen (1990) for pattern recognition. The
fundamental idea is to divide the input space into number of distinct regions,
called decision regions.
These different ANN structures are to predict the malignancy of the
different cancers.
3.2.2 Wavelet Neural Network
Multilayer perceptron (MLP) along with the back propagation
learning algorithm is the most popular type of ANN among all in practical
situations (Zainuddin et al 2001, Shirvany et al 2009). However,
disadvantages of a MLP are
1. difficulties in reaching the global minimum in a complex
search space
2. time-consuming and
3. failure to converge when high nonlinearities exist,
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To overcome the deficiencies of an MLP, a Wavelet Neural
Network (WNN) has been introduced as a vital alternative to the MLP
(Banakar et al 2008). Wavelet families are integrated as the activation
function in the hidden layer of WNNs. There are several issues that are
concerned with WNNs, varying from different learning algorithms, network
architecture, type of activation functions used in hidden layer and also the
parameter initialization.
A proper initialization of the network parameter is a key factor to
achieve faster convergence rate and higher accuracy rate. Approaches of using
an explicit expression, hierarchical clustering, support vector machine,
genetic algorithm and K-Means clustering are among the approaches that
have been implemented in the parameter initialization (Maoan et al 2004,
Xiao-Guang et al 2006). Various clustering algorithms, namely, K-Means
(KM), Fuzzy C-means (FCM), symmetry-based K-Means (SBKM),
symmetry-based Fuzzy C-means (SBFCM) and modified point symmetry-
based K-means (MPKM) clustering algorithms are available in initializing the
WNN translation parameter. These various clustering algorithms can be
integrated into the WNN and applied in a real world application, where the
classification problem of heterogeneous cancer using the microarray data is
the main concern.
3.2.3 Probabilistic Neural Network (PNN)
PNN was developed by Specht (1988, 1990). This provides a
common solution to pattern classification problems by following the
probabilistic approach based on the Bayes’ formula. The Bayes’ decision
theory emerged from his formula takes into account the relative likelihood of
events and uses ‘a priori’ information to improve prediction. Parzen
estimators are used by the network model to attain the corresponding
probability density functions (p.d.f.) to the classification categories.
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Parzen (1962) showed that classes of p.d.f. estimator asymptotically approach
the fundamental density function, provided that it is continuous. Cacoulos
(1966) extended Parzen’s approach to the multivariate case.
A supervised training set is used by PNN to develop probability
density functions within a pattern layer. Training of a PNN is much easier
than other ANN techniques. Key advantages of PNN are that training needs
only a unique pass and that the decision hyper-surfaces are guaranteed to
approach the Bayes-optimal decision boundaries as the number of training
samples grows. On the other hand, the main limitation of PNN is that all
training samples must be stored and used in classifying new patterns. But, in
order to decrease the computational cost, dimensionality reduction and
clustering approaches are usually applied, previous to the PNN construction.
The PNN-based decision approach was applied to categorize a
group of individuals into certain categories of diagnosis in the area of cancer
diseases.
3.2.4 Hopfield Artificial Neural Network (HANN)
Hopfield Neural Network (HANN) is one of the ANN, which has
been used in many of the literatures for different purposes. The main use of
Hopfield Neural Network in the medical image processing field is its use
for classification of Magnetic Resonance (MR) images of the brain based
on energy minimization as described in (Armatur et al 1992). The
performance of the HANN is found to be significant. The algorithm is
enhanced to overcome some of the problems such as considering the
minimization of the sum of squared errors and ensuring the convergence of
the network in a pre-specified period of time.
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The improved version of the HANN has been used in (Sammoda et al
1996) for MR images of the brain. The same algorithm is used for the
segmentation of the extracted lung regions. Then the extracted lung regions
segmentation problem is formulated as a minimization of an energy function
constructed of a cost-term as a sum of squared errors. In order to guarantee the
convergence of the network, the minimization is achieved with a step function
permitting the network to reach its stability in a pre-specified period of time.
Figure 3.2 Architecture of HANN
The HANN architecture (Figure 3.2) consists of a single layer
representing a grid of N x M neurons with each column representing a class
and each row representing a pixel. All neurons work as both input and output
neurons simultaneously. In fact neurons under each class hold the probability
that the corresponding pixel belongs to this class. N is the size of the given
image and M is the number of classes that is given as ‘a priori’ information.
The network is designed to classify the feature space without teacher, based
on the compactness of each class calculated using the distance measure (Rkl)
between the kth
pixel and the centroid of class l. The problem of segmentation
is formulated as a partition of N pixels among M classes such that the
assignment of the pixels reduces the cost-term of the energy (error) function:
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E = R V (3.1)
where Rkl represents the distance measure between the kth
pixel and the
centroid of class l, and defined as follows:
R = ||X X || (3.2)
where Xk is the feature value (intensity value) of the kth
pixel and X l is the
centroid value of class l, and defined as follows:
X = (3.3)
where nl is the number of pixels in class l. Considering n=2 which means the
energy is defined as sum-squared error. Vkl is the output of the klth
neuron.
This approach adopted the winner-takes-all learning rule, where the input-
output function for the kth
row (to assign a label ‘m’ to the kth
pixel) is given
by:
V (t + 1) = 1 if U = Max[U ,1]
V (t + 1) = 0 otherwise (3.4)
The minimization achieved by using Hopfield neural network
(HANN) and by solving a set of motion equations satisfying the condition,
( t) (3.5)
where Ui and Vi respectively represent the input and output of the ith
neuron,
(t) represents scalar positive function of time, which determines the length
of the step to be taken in the direction of the vector d = E(V ). The suitable
selection of the step (t) is done by proper skill. Experimentation and a
familiarity with a given class of optimization problems are often required to
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find the best function. It is found that the (t) function used by Sammouda et al
(1996) for segmenting the MR data using HANN is used in this approach and
works fine for segmenting the CT data:
(i.e) ( t) = t (T t) (3.6)
where t represents the iteration step and is the pre-specified convergence
time. HANN segmentation algorithm can be summarized in the following
steps:
1. Initialize the input of neurons to random values.
2. Apply the input-output function (Vkl) defined above, to obtain
the new output values for each neuron, establishing the
assignment of pixels to classes. The class membership
probabilities grow or diminish in a winner-takes-all style as a
result of contention between classes. In winner-takes-all
model, the neuron with the highest input value fires and takes
the value 1, and all remaining neurons take the value 0.
3. Compute the centroid (X l) as defined above, for each class l.
4. Compute the energy function (E) as defined above.
5. Update the inputs (Ui) using the following equation, Learning
occurs when neuron input weights are adjusted in an attempt
to reduce the output error.
U (t + 1) = U (t) + (3.7)
6. Repeat from step 2 until t = Ts. This process iteratively
modifies the pixel label assignments to reach a near optimal
final segmentation map.
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Equation 3.1 to 3.7 explains the application of HANN to
segmentation of MRI images.
The existing neural network techniques are susceptible to a high
rate of false positives as well as false negatives. Thus a novel technique is
necessary to increase the accuracy, specificity, and sensitivity of the system.
3.3 ARTIFICIAL INTELLIGENCE (AI)
The most efficient method to overcome the drawbacks of the
existing approaches is to use Artificial Intelligence. Fundamentally, image
processing is followed by fuzzy logic. These AI techniques remove the human
errors in detection and the number of steps involved is also less. And more
importantly it is cost effective.
Artificial intelligence has been widely used for logistics, data
mining, medical diagnosis and several other areas. The popularity of AI is due
to the factors like the incredible influence of computers, a superior
prominence on solving specific sub problems, the formation of new binds
between AI and other fields working on related problems etc., AI technique is
expected to display the capabilities like deduction, reasoning and problem
solving, knowledge representation, planning, perception, creativity etc.
3.3.1 Importance of Fuzzy Logic
Supervised methods are extensively used in medical image
segmentation, but they need conditions that are difficult to satisfy the medical
field (Francesco Masuli et al 1999).
Fuzzy logic can be widely used for controlling a process that is too
non-linear or too misunderstood to use a traditional control designs.
Therefore, it can be used to deal with complex systems. Moreover, fuzzy
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logic facilitates control engineers to easily implement control approaches used
by human operators.
Fuzzy logic offers an efficient way to reach a definite solution
based upon unclear, ambiguous, imprecise, noisy or missing input
information. It can be easily implemented in hardware, software or a
combination of both. It uses an imprecise but very descriptive language to
deal with input data more like human operator. It provides a foundation for
the development of new tools for dealing with natural languages and
knowledge representation.
Clustering is the process of dividing data elements into classes or
clusters so that the data in the same class are similar and data in different
classes are dissimilar. In case of hard clustering, the elements belong to
exactly one class and in the case of soft clustering or fuzzy clustering, the
element can belong to more than one class and associated with each element
is a set of membership levels. Fuzzy clustering is the process of assigning the
membership value and using them to assign elements to one or more clusters.
3.3.2 Fuzzy C-Means (FCM)
The commonly used fuzzy method for clustering is Fuzzy C-Means
(FCM). The aim of the objective function of FCM is to find the cluster
centers and to produce class membership matrix, which designates a
membership to a data point, depending on the relativity of the data point to a
particular class when compared to other classes. (ie) The objective function
provided by FCM is
Jm (U, v) =c
i
ikm
ki
N
k
AvX
1
2
1
)(
(3.8)
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where X={x1, x2,…xN} RN in a dataset, c is the number of clusters in X: 2
c < N, m is a weighting exponent: 1 m < , U = { ik } is the fuzzy c-
partition of X, AvX ik is an induced a-norm of RN, and A is a positive-
definite (NXN) weight matrix.
3.3.3 Possibilistic C-Means (PCM)
FCM treats the image as separate data points. Hence, an alternate
method called Possibilistic C-Means (PCM) is used. The Possibilistic
C-Means algorithm uses a Possibilistic type of membership function to
illustrate the degree of belonging. It is advantageous that the memberships for
representative feature points be as high as possible and unrepresentative
points have low membership. The intention function, which satisfies the
requirements, is formulated as follows,
(3.9)
where, dij represents the distance between the jth
data and the ith
cluster center,
ij denotes the degree of belonging, m represents the degree of fuzziness, i is
the suitable positive number, c is the number of clusters, and N denotes the
number of pixels. The main advantage of this PCM technique is that the value
of i can be fixed or can be changed at each iteration. The PCM is more
robust in the presence of noise, in finding valid clusters, and in giving a robust
estimate of the centers. The integration of FCM and PCM in Fuzzy
Possibilistic C-Means could provide much better result.
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Lung Regions Extraction
Segmentation of lung region using
FPCM
Analysis of segmented lung region
Formation of diagnosis rules
Testing and Evaluation
Because of various advantages of the AI and fuzzy logic, the
proposed system uses the Fuzzy Possibilistic C-Means (FPCM) for the
segmentation purpose.
3.4 PROPOSED METHODOLOGY
A new method for segmenting Lung cancer images is presented as
in Figure 3.3. This system is used for detection of lung cancer by analyzing
chest computer tomography (CT) images. In the first stage of this CAD
system, pure basic image processing techniques are used to extract lung
regions. The extracted lung regions in each slice are segmented using Fuzzy
Possibilistic C-Means (FPCM) and it shows good segmentation results in a
short time. FPCM algorithm is presented that incorporates spatial information
into the membership function for clustering.
Figure 3.3 The Lung Cancer Detection System using FPCM
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3.4.1 Lung Regions Extraction
The main limitations of the earlier gray level thresholding
techniques are the problem of selecting suitable and accurate threshold values.
Moreover, some approaches, as in (Kanazawa et al 1998) need a post
processing step to compensate the lost parts that may occur as a result of
using the thresholding technique. To overcome the problems of the
thresholding methods, a new method is used for the automatic extraction of
lung regions based on one of the different features of the raw data obtained
using the bit-plane slicing technique. The extraction approach described in
this section is fully automatic and depends on a set of basic digital image
processing techniques adapted to the CT data. A CT image of chest contains
different regions such as the background, lung, heart, and liver and other
organs’ areas. The main aim of lung region extraction step is to separate the
lung regions, the regions of interest (ROIs), from the surrounding anatomical
structures.
Figure 3.4 clearly explains the method (Sammouda et al 2006) for
the extraction of the lung regions from CT chest image. Initially, the bit-plane
slicing algorithm (Gonzalez et al 2002) is applied to each CT image of the
raw data. The resulting binary slices are then examined to select the best bit-
plane image that helps in extracting the lung regions from the raw CT-image
data with a certain degree of accuracy and sharpness.
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Original Image Bit-Plane Slicing Erosion Median Filter Dilation
Outlining Lung border Extraction Flood Fill Algorithm Extracted Lung
Figure 3.4 The Lung regions extraction method
In order to refine the selected bit-plane image, other approaches
were used for different purposes in a sequence of steps. The main aim of
erosion, median filter and dilation steps is to eliminate irrelevant details that
may add extra difficulties to the lung border extraction process. The outlining
step mainly aims to extract the structure’s borders. The lung border extraction
step is used to separate lung structure from all other uninteresting structures.
Finally, a stack-based flood-fill approach is used to fill the extracted lung
regions with their original intensities. Figure 3.5 shows the results of applying
step by step, the lung regions extraction method to a given CT image.
3.4.2 Lung Regions Segmentation
After extracting the lung regions successfully from the raw CT
images, as described in the previous section, the second step of the proposed
CAD system is lung regions segmentation that aims to segment the extracted
lung regions searching for cancerous cell candidates – the new Region Of
Interests (ROIs). A huge number of candidates are chosen with large number
of non-cancerous candidates or false positives and a few numbers of
cancerous candidates.
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Figure 3.5 Lung regions extraction algorithm: a. original CT image,
b. bit-plane, c. erosion, d. median filter, e. dilation,
f. outlining, g. lung region borders, and h. extracted lung.
There are various segmentation techniques available in the
literature which can be used for this purpose. But FPCM is used here due to
its significant performance.
Fuzzy Possibilistic C-Means (FPCM)
FPCM is a clustering algorithm that integrates the characteristics of
both Fuzzy and Possibilistic C-Means. Memberships and typicalities are very
vital for the correct feature of data substructure in clustering problem. Thus,
an objective function in the FPCM depending on both memberships and
typicalities can be shown as:
J (U,T,V) = (u + t )d (X ,v ) (3.10)
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With the following constraints:
= 1, {1,…,n} (3.11)
t = 1, {1,…,c} (3.12)
A solution of the objective function can be obtained through an
iterative process where the degrees of membership, typicality and the cluster
centers are updated using
u =
( , )
( ( , )
( )
,1 c,1 n (3.13)
t = =
( , )
( , )
( )
,1 c,1 n (3.14)
v =( )
( ),1 c (3.15)
Equations 3.8 to 3.15 explain the FPCM algorithm. FPCM
produces memberships and possibilities simultaneously, along with the usual
point prototypes or cluster centers for each cluster. FPCM is a hybridization
of Possibilistic C-Means (PCM) and Fuzzy C-Means (FCM) which provides
the solution to various problems.
The advantages of the FPCM method are the following:
Provides regions more homogeneous than other techniques
it reduces the spurious blobs
it removes noisy spots
It is less sensitive to noise than other techniques.
For the segmentation of lung images, the weighting exponent (m)
has been chosen as 2, the typicality weight ( ) was chosen as 2 and
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termination threshold was fixed as le-5 and the maximum number of
iterations was fixed as 100.
Figure 3.6 Input feature vectors of an image
Figure 3.7 Clustered feature vectors using FPCM
0 20 40 60 80 100 120 1400
20
40
60
80
100
120
140Input feature vectors
0 20 40 60 80 100 120 1400
20
40
60
80
100
120
140Clustered feature vectors (FPCM)
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Figure 3.6 shows the graphical representation of the feature vectors
of a lung image and Figure 3.7 shows the plot of the clustered feature vectors
using FPCM.
Figure 3.8 Number of iterations taken by FPCM
Figure 3.8 shows the number of iterations taken by FPCM to
achieve the desired result.
FPCM with the specifications mentioned above is applied to each
of the extracted lung regions for the whole data set and the results are
maintained for further processing in the following steps. FPCM segmentation
results are accurate and homogeneous and it takes shorter time to achieve the
desired segmentation results. FPCM needs less than 100 iterations to reach the
targeted results.
3.4.3 Features Extraction and Formation of Diagnostic Rules
The segmentation results were obtained and the approach initiated
by initial cancerous candidate objects or nodules that denote all the members
of one of the classes resulting from the FPCM segmentation approach. The
objects are labeled and different features are extracted for those objects and
0 10 20 30 40 50 60 70 80 900
50
100
150
200
250
300
350Termination measure (FPCM)
Iteration num.
Term
ina
tion
me
asure
va
lue
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they are used in the following diagnostic step, where some diagnostic rules
are formulated to remove a huge number of false candidates that usually
results from the segmentation step.
3.4.3.1 Feature Extraction
Different features are extracted for the labeled candidates. They are
Area, Centroid, Filled Area, Perimeter, Radius and the average intensity.
Among these features extracted, the following features are selected and used
for framing the diagnostic rules (Sammouda et al 2005)
1. Area of the candidate region
2. The Maximum Drawable Circle (MDC) inside the candidate
region
3. Average intensity value of the candidate region.
It is found that, these features are suitable to achieve accurate
diagnosis experimentally. The first feature (the area of the candidate region or
object) is used to eliminate isolated pixels (seen as noise in the segmented
image), eliminate very small candidate object (Area is less than a threshold
value). This feature generally eliminates a good number of extra candidate
regions that do not have a chance to form a nodule; moreover it also reduces
the computation time needed in the following steps.
The second feature is to denote each candidate region by its
equivalent MDC. This method begins to draw a circle starting from a point
inside a candidate region or object. This circle should accomplish the state
that all the pixels inside the circle belong to the object in process. All the
pixels inside the object are regarded as starting drawing point. The process
starts to draw one-pixel radius size circle starting from a point inside the
candidate region. If the process succeeds, the radius is increased by one pixel
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and tries to redraw the circle. This process is repeated until the last radius
exceeds the border of the region. Each candidate object saves its circle to be
used in the diagnostic process to eliminate more and more false positive
cancerous candidates.
The average CT intensity value of the candidate region is the third
feature and is used to remove more regions that do not have features of
cancerous cells. The average intensity value denotes the average intensity
value of all the pixels that belong to the same region (object) and is calculated
as follows:
average(j) =( )
where ‘j’ denotes the object index and ranges from 1 to the total number of
candidate objects in the whole image. Intensity (i) denotes the CT intensity
value of pixel ‘i’, and ‘i’ ranges from 1 to ‘n’, where ‘n’ represents the total
number of pixels belonging to object ‘j’.
3.4.3.2 Formation of Diagnostic Rules
Diagnostic rules are formulated for the extracted features and they
are used in the proposed CAD system.
Rule 1: When the area of the object is below the threshold value
‘T1’ for each candidate object, then it is deleted from the candidate list. When
this condition is applied, it has the effect of reducing the number of false
positives that exist in the initial candidate objects.
Rule 2: If the value of MDC of an object is below the threshold
value ‘T2’, then it is deleted from the candidate list. ‘T2’ is chosen to be 2-
pixels radius size, thus any candidate objects with less than 2-pixels radius
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size would be removed as it is away from being a nodule, and very close from
being a blood vessel. This rule is associated with the medical fact that true
lung nodules show certain circularity especially small lung nodules. When
this condition is applied, it has the effect of removing a large number of
vessels, which in general have a thin oblong, or line shape.
Rule 3: For each candidate object, if the value of the average
intensity of this object lies outside a particular range, i.e. between ‘T3’ and
‘T4’, then it is deleted from the candidate list. The right values chosen for both
thresholds ‘T3’ and ‘T4’ are based on Medical information and
experimentation. The proposed approach used the values of -9000 CT-
intensity value for the threshold ‘T3’ and -12500 CT intensity value for the
threshold ‘T4’. The rule has the effect of removing a few more false positives.
The lung nodule detected for a CT image is shown in Figure 3.9.
Figure 3.9(a) represents the original CT scan image and 3.9(b) represent the
cancer nodule detected image after segmentation and application of diagnosis
rules.
(a) (b)
Figure 3.9 (a) Original Image (b) Lung Nodule Detected after
Segmentation
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3.4.3.3 Early Detection
The diagnosis rules formulated are very much useful in detecting
the cancerous regions at the early stage. The threshold values used in the
diagnosis rules helps in detecting the cancerous region at its starting stage
(i.e. when the cancer region is very small).
After all the rules are applied, very small numbers of cancerous
candidate objects are present. These remaining candidates are marked by the
CAD system as possible cancerous regions. Then the images with these
regions were reported and exhibited to radiologist to take the final decision.
This shows that, the purpose of the proposed CAD system is not to replace
radiologists; but to assist radiologists and offer a tool that facilitates them in
detecting lung cancer at early stages by alerting them to possible
abnormalities. Moreover, the proposed approach aims at improving the
accuracy of detection and minimizing the time spent by radiologists in
analyzing a vast number of slices per patient.
3.5 DATASET
The experiments were conducted on the proposed computer-aided
diagnosis systems with the help of real time lung images. Nearly, 500 real
time images of different categories of people were collected for the study.
These images included both benign and malignant nodules. Among these
images, 270 patients with no signs of cancer are left out and remaining 230
patients’ images are analyzed for the occurrence of benign and malignant
nodules. Malignant nodules are cancerous while benign nodules are not
cancerous. The images are taken from CT scanner with slice thickness 1 mm
with voltage peak 120kVp, tube current 220mA, with a scan rate of 300 slices
per minute. Each image is stored as an array of type unsigned integer of size
512 x 512 pixels.
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Patients in the age group of 35-50 are likely to have benign nodules
(99 % are non-cancerous nodules) whereas patients above the age group of 50
are likely to develop malignant nodules. In the case of gender, males are
mostly affected by lung cancer than female.
With the ground truth provided by the radiologist, a dataset
consisting of 300 nodules were taken up for the study. Among these 300
nodules, 123 are malignant and 177 are benign. The size of the nodules ranges
from 5mm to 10 mm and among the 123 nodules, 28 nodules have a size
greater than or equal to 2mm.
3.6 PERFORMANCE EVALUATION
The performance evaluation of this approach can be estimated
based on sensitivity, specificity and accuracy
Sensitivity = TP / (TP + FN)
Specificity = TN / (TN + FP)
Accuracy = (TP + TN) / (TP + FP + TN + FN)
The true positives (TPs) identified by this approach was 94 and
hence the sensitivity was reported as 76.42% (94/123). The number of false
positives (FPs) was 38 and hence the specificity was reported as 78.53 %
(139/177). Accuracy was estimated as 77.67% (233/300).
3.7 SUMMARY
This chapter discusses about an automatic CAD system for early
detection of lung cancer by analyzing raw chest CT images. The approach
starts by extracting the lung regions from the CT image using several image
processing techniques, including bit plane slicing, erosion, median filter,
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dilation, outlining, and flood-fill algorithm. A novel approach of using bit-
plane slicing technique is introduced instead of the thresholding technique
that is used as the first step in the extraction process to convert the CT image
into a binary image. Bit-plane slicing technique is both faster and data- and
user-independent compared to the thresholding technique. After the extraction
step, the extracted lung regions are segmented using Fuzzy Possibilistic
C-Means (FPCM) algorithm. The FPCM algorithm shows homogeneous
results obtained in a short time. Then, the initial lung candidate nodules
resulting from the FPCM segmentation are analyzed to extract a set of
features to be used in the diagnostic rules. These rules are formulated in the
next step to discriminate between cancerous and non-cancerous candidate
nodules. This technique is a powerful method for noisy image segmentation
and works for both single and multiple-feature data with spatial information.
The extracted features in the proposed system are: the segmented lung
regions, the Maximum Drawable Circle (MDC) and the average intensity
value of the region.
The next chapter deals with the next proposed segmentation
scheme called “Segmentation of Lung Region using Modified Fuzzy
Possibilistic C-Means (MFPCM)”.