a review on computerized pulmonary nodule detection …

1

A REVIEW ON COMPUTERIZED PULMONARY NODULE DETECTION IN CT

IMAGES

1P. Malin Bruntha,

2S. Immanuel Alex Pandian

1Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences,

Coimbatore, Tamilnadu, India

2Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences,

Coimbatore, Tamilnadu, India

[email protected]

Abstract

Cancer is one of the deadliest diseases in the world. Lung cancer, a type of cancer is

the leading cause of deaths among cancer deaths. Early detection and diagnosis of the disease

is essential to prolong the life of the patients affected with this scourge. To facilitate this,

Computer Aided Detection and Diagnosis systems have emerged. Many authors have applied

various techniques to successfully detect and diagnose the pulmonary nodules. A systematic

review of these techniques is the need of the hour. This paper aims to address these critical

concerns.

Key Words: Lung Pulmonary Nodules, Preprocessing, Segmentation, Nodule Detection,

Classification.

1. Introduction

Cancer is a leading cause of death among diseases in human beings [1]. According to

International Agency for Research in Cancer, World Health Organization, in the year 2012

alone, 14.1 million newer cancer cases were detected [2]. Among the various types of cancer,

lung cancer is the leading cause of cancer deaths worldwide. The occurrence of lung cancer

worldwide is 13% (out of 14.1 million cases). It is about 15.5% (out of 6763030) in Asia and

6.9% (out of 1014934) in India [3].

The people who are diagnosed with lung cancer have a 5 year survival rate in between

10-16% if the disease is in advanced stages. But if it is detected at early stages, the 5 year

International Journal of Pure and Applied MathematicsVolume 119 No. 18 2018, 2753-2771ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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survival rate increases dramatically to 70% [4]. This underscores the need for early detection

of lung cancer by the latest technological tools at our disposal.

A pulmonary lung nodule is defined as having focal opacity of 3 to 30 mm diameter

nodules. If the sizes are less than 3 mm, then they are termed as micro-nodules. If the sizes

are greater than 30 mm, then they are termed as “mass” [5]. It can be classified further with

respect to their position and location [6]. These lung nodules may not have any attachment

with neighbouring structures with well circumscribed nodules. Juxta-pleural nodules are

attached to lung parenchyma and juxta-vascular nodules are attached to vessels. These

nodules can be solid, sub-solid and in some cases, non-solid [7].

It is imperative to calculate the nodule size carefully since it is essential to determine

the malignancy factor. Inaccuracies may creep in if the nodules are non-spherical and if they

are measured manually [8,9]. Furthermore, the efforts taken to improve the detection of

cancer nodules from the available images by manual methods may have the following

difficulties like limitations of human visual systems, insufficient training given to the

radiologists who handle the images and fatigue of medical personals [10]. The workload of a

given radiologist may also indirectly affect his/her efficiency [11]. These may lead to

misinterpretation of data or may result in error in judgment. Hence, it is essential to have

automated methods to detect, measure and diagnose these pulmonary modules.

Computerized Tomography (CT) which became popular and more available in 1970s

has become one of the most important modality for imaging small lung modules. The people

with high risk of getting lung cancer such as smokers have been screened with low-dose CT

(LDCT) scans in order to facilitate early detections of lung cancer [12]. A major challenge is

to detect, segment and classify the nodules that will assist the radiologists in pinpointing the

possible existence of abnormalities. Such systems are called as Computer Aided Diagnosis

(CAD) systems. They provide specific information about these nodules [13].

One can classify CAD systems into two types. The first one is Computer Aided

Detection system (CADe) and the second one is named as Computer Aided Diagnostic

system (CADx) [14]. Through CADe system, the radiologists can identify Regions of Interest

(RoI) in the given image that reveals malignancy. By using CADx system, one can come to

know about the identification of the disease, its type and its severity. By using CADx, it is

possible to tell the stage of cancer and its progression or regression. CT scans can be used to

diagnose the level of malignancy. This would avoid unnecessary repeated CT scans.

International Journal of Pure and Applied Mathematics Special Issue

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In short, CADe systems can efficiently reduce the workload of radiologists and also

reduce the time taken for analyzing a particular image thereby enabling them to screen more

patients within a given span of time. They can assist efficiently in early detection of lung

cancer. They can improve the pulmonary nodule detection accuracy [7].

There have been many systematic reviews in the past done by many authors on this

subject in order to analyze and appreciate the performance of the best techniques available

and developed up to that point of time. For example, Lee et al., has carried out a systematic

review on automated detection of lung nodules from CT images [6]. Suzuki et al., has

undertaken a profound review of CADe in thoracic and colonic imaging [15]. Eadie et al., has

critically reviewed the usage of CADx in diagnostic cancer imaging [16]. Few other reviews

were carried out by Firmino et al [7], El-Baz et al [17] and Igor Rafael S. Valente et al [13].

But, it is necessary to conduct a critical review time and again in order to include the latest

techniques developed very recently. It is with this aim, this review of literature has been

carried out. For the review of literature, a total of 70 works from Web of Science, PubMed,

IEEE Xplore, Science Direct and others have been used.

2. Data Acquisition

The preliminary step in any image processing is getting images from the source. In

this review, the papers which used CT imaging modality are collected. Few papers used the

private datasets of lung CT images, obtained from hospitals and from national screening

programs. The available public databases are very helpful to the researchers to validate their

algorithm. The popular lung CT databases are LIDC-IDRI, NELSON, ELCAP, SPIE-AAPM,

Kaggle’s Data Science BOWL 2017 and ITALUNG-CT. Figure 1 represents a typical lung

CT image.

Figure 1. Lung CT image


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3. Pre-processing

Pre-processing is a preliminary process in image processing to enhance the quality of

the input image. This process eliminates the extraneous details present in the lung CT image.

This step ensures the given image is free from noise and any artefacts. The following pre-

processing methods are mostly used in the reported papers. They are: Gray level

thresholding [18,32,40],Gaussian smoothing [40,54,69] andTrilinear Interpolation

[26,35,37,41].

Other techniques used in many other papers are Morphology Operation[20], Streak

Detection Filter[21], 2D enhancement filters[24], Isotropic resampling [25], Cylinder Filter,

Spherical Filter [29,34], Convolution with a Gaussian kernel [31], 2D flooding [40], selective

filters [41],Angiometric diffusion model [42], Down sampling, Contrast enhancement [44],

Anisotropic diffusive filter [49,60,63], Median filter [53,54,64], 3D Coherence Enhancing

Filter [54], Contrast Stretching [68], Histogram Equalization [69], Discrete wavelet transform,

Unsharp energy masking [71], Type II fuzzy algorithm [76] and Bicubic interpolation [77].

The preprocessing techniques are listed in Table 1 according to the chronological order.

Table 1. Literature review of preprocessing

Authors Techniques used

Samuel G. Armato et

al [18]

Grey level profile

Tomakazu Oda et al

[20]

Morphology operation

Mitsuru Kubo et al

[21]

Streak Detection filter

Qiang Li et al [24] 2D enhancement filters

William J. Kostis et

al [25]

Isotropic Resampling

David S. Paik et al Trilinear interpolation

Sukmoon Chang et

al [29]

Cylinder filter, spherical filter

Paulo R. S.

Mendonca et al [31]

Convolution with a Gaussian kernel


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TemesguenMessay

et al [44]

Down sampling, contrast enhancement

Stefano Diciotti et al

[49]

Anisotropic diffusive filter

Ezhil E. Nithila et al

[69]

Histogram equalization, Gaussian filtering,

Farzad V Farahani et

al [76]

Type II fuzzy algorithm

4. Lung Segmentation

Segmentation is the process of separating the lung parenchyma from the lung CT

image. Before detecting the required candidate nodules, in most of the papers, the lung

region was extracted based on thresholding, shape and border. The popularly used

segmentation techniques were summarized from the selected papers. They are Gray level

histogram [18], Simple thresholding [19,21,24,26,44,49,51,61,63,64], Labelling

[21,43],Gray-level thresholding [22,32,68], Dilation [26,32,43], 3D region growing algorithm

[31,77], Adaptive thresholding [43,47],2D region growing algorithm with rolling ball

algorithm [46,52,60],3D connected component analysis [59,61,63], Fuzzy c-means algorithm

[60,71].

The other segmentation techniques used in the papers are Negative Masking

[26],Linear discriminant analysis [27], Region growing algorithm with active contour model

[36], Genetic cellular neural network [39], Inner border tracing algorithm and adaptive border

marching algorithm [40], Fuzzy thresholding [42], Erosion[43], Coupled competition and

diffusion process [48], Robust active shape model matching algorithm [50], Greedy snake

algorithm [51], Statistical intensity based approach [55], Otsu thresholding [56,70,77],

morphological opening [56], Active contour model with level set method [58], Optimal

thresholding, , Adaptive curvature thresholding [60], high level vector quantization [61],

Morphological closing[61,63], Active contour model with signed pressure function [69],

entropy algorithm [71], Modified spatial kernelized fuzzy c-means clustering [76]. In Table 2,

the segmentation algorithms are listed as per the chronological order.


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Table 2. Literature review of segmentation

Authors Segmentation

Hidetaka Arimura et al [27] Linear discriminant analysis

Paulo R. S. Mendonca et al [31] 3D region growing algorithm

SerhatOzekes et al [39] Genetic cellular neural network

Jiantao Pu et al [40] Inner border tracing algorithm, adaptive border

marching algorithm

J.R.F. da Silva Sousa et al [46] 2D Region Growing Algorithm, Rolling ball

algorithm

Shanhui Sun et al [50] Robust Active Shape Model Matching Algorithm

Elizabeth et al [51] Thresholding and greedy snake algorithm

Jinsa Kuruvilla et al [56] Otsu thresholding, morphological opening

TemesguenMessay et al [62] 3D global segmentation, multiple successive 2D

rolling ball filters

Ezhil E. Nithila et al [69] New active contour model with new signed

pressure function

Farzad V Farahani et al [76] Modified Spatial Kernelized Fuzzy C-Means

clustering

Jing Gong et al [77] Otsu thresholding, 3D region growing,

morphological operations

5. Candidate Nodule Detection

After segmenting the lung region, the region of interest (ROI) will be identified and

segmented from the other parts of the lungs. This part is very crucial because some nodules

are attached with pleural walls while some nodules are attached with blood vessels. Any dot

or blob like structures are segmented using the following prevalent techniques viz.,

Multiplegray level thresholding [18,25,27,35,44], 2D connected component labelling method

[24,25], Thresholding [29,34,41,44],Region growing algorithm along with connected

component analysis and morphological operation [32,36] and 3D Region growing algorithm

[37,41,48].

Other techniques for nodule detection are Genetic algorithm template matching

[19,28], 3D labelling method [20], Laplacian filters [21],Surface normal overlap [26],


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Matched filter, Ring filter [27], 3D morphological matching algorithm [32], Gradient

thresholding[35], Sphericity oriented contrast region growing and fuzzy connectivity map

[38], 8 directional search method with distance threshold [39], Adaptive thresholding [43],

3D labelling technique [43], Expert filtering [44], Skeleton based segmentation [46],

Marching Cube algorithm with radial basis function [47], Laplacian of Gaussian filters[49],

Erosion filter and pruning process [51], Growing neural gas [52], Optimal thresholding [54],

Expectation maximization algorithm with level set approach [55], Dot enhancement filter

[59],K-means clustering [68], Fuzzy c-means clustering [69], Spatial fuzzy c-means

algorithm [70], Gaussian smoothing [71], 3D region growing GrowCut algorithm [74], 3D

tensor filtering and local shape feature analysis [77]. Table 3 gives few of the important

nodule detection methods used by different authors.

Table 3. Literature review of Nodule detection

Authors Nodule Detection

Samuel G. Armato et al [18] Multiple Gray Level thresholding

Yongbhum Lee et al [19] Genetic Algorithm Template matching

Qiang Li et al [24] 2D connected component labelling

technique

William J. Kostis et al [25] Gray level thresholding, connected

component analysis, vascular subtraction,

pleural surface removal technique

Paulo R. S. Mendonca et al [31] Geometric and intensity model, eigen

values of curvature tensor

Kyontae T. Bae et al [32] 3D morphological matching algorithm

Xujiong Ye et al [42] Adaptive thresholding, modifier

expectation-maximization method

Jorge Juan et al [43] Adaptive thresholding, area test, 3D

labelling technique

Stelmo et al [52] Growing neural gas (GNG), 3D distance

transform

Amal A. Farag et al [55] Expectation Maximisation Algorithm,

variational level set approach

Muzzamil Javaid et al [68] K-means clustering, morphological


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opening

Jing Gong et al [77] 3D tensor filtering and local shape feature

analysis

6. Classification

Once the nodule candidates are extracted from the CT image, it is imperative to

classify whether it is normal nodule or abnormal nodule. This step is considered as the most

important step in CAD system because based on this, the radiologist can decide to treat and

follow up the patients. The features such as geometric, shape, texture is extracted from the

candidate nodules and these handcrafted feature vectors are given as input to the classifier.

For the deep learning convolution neural network, the feature extraction process is not

required [62,66,72,73].

The classification process is often considered as false positive reduction. The

following classifiers are popular in taking decisions between true nodule and false nodule.

They are: Linear Discriminant Analysis [18,22,27,57], Rule Based Classification [19-

22,27,31,32,41,42,43,58,61,68], Multiple Massive Training Neural Network [23,27,30], K-

nearest neighbour [34,74], SVM [42,46,52,54,59,61,65,67,68,77], Random Forest [45,70,77],

and Radial basis function neural network [51,74].

Other techniques adopted for reducing false positives are Area based

classification[24], Bayesian supervised classifier [28], double threshold cut and neural

network [36], 3D template using convolution based filtering and fuzzy rule based

thresholding [37],Fisher Linear Discriminant Classifier and Quadratic Classifier [44], Back

propagation neural network(BPNN) [56], Gentle Boost Classifier [57], Multi-layer

perceptron regression neural network [62], Multi-view convolutional neural network [66],

Particle swarm optimized BPNN [69], Multi-crop convolutional neural network [72], 3D

convolutional neural network [73], Naïve Bayes classifier [74], AdaBoostedBPNN [75],

Ensemble of multilayer perceptron, k-nearest neighbour and SVM [76], Logistic Regression

[77] and J48 Decision Tree [77]. The performance of the classifier/false positive reduction

methods are listed in the Table 4.


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Table 4. Literature review of classifiers

Authors Classifier Performance

Kenji Suzuki et al

[23]

Multiple Massive Training

Neural Network

Sensitivity 80.3%

No. of FP=0.18/case

Hidetaka Arimura et

al [27]

2 rule based scheme, multi-

MTANN, LDA

Sensitivity 83%

No. of FP=5.8/scan

Aly Farag et al [28] Bayesian supervised

classifier

Sensitivity 82.3%

No. of FP=9.2/scan

Jinghao Zhou et al

[34]

K-nearest neighbour Mean error rate 3.7%

No. Of FP = 1/scan

Bellotti et al [36] Double threshold cut and

neural network

Sensitivity 88.5%

No. of FP=6.6/scan

Jorge Juan et al [43] Linear Discriminated

analysis (LDA)

Sensitivity 80%

No. of FP=7.7/case

TemesguenMessay

et al [44]

Fisher linear discriminant

classifier, quadratic

classifier

Sensitivity 82.6%

No. of FP=3/scan

Lee et al [45] Random Forest Sensitivity 98.33%

Specificity 97.11%

Tong Jia et al [58] Rule based classification Sensitivity 90%

No. of FP=1/Scan

Hao Han et al. [61] Rule based filtering,

feature based SVM

Sensitivity 89.2%

No. of FP=4/scan

Arnaud A.A. Setio et

al [66]

Multi-view convolutional

neural networks

Sensitivity 85.4% –

90.1%

No. of FP=1-4/scan

Qi Dou et al [73] 3D convolutional neural

network

Sensitivity 92.2%

No. of FP=8/scan

7. Conclusion

The authors have presented a critical review of literature with regards to CT scans of

lung nodule detection and classification using computer aided diagnosis methods. This


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research review has included research papers published in peer reviewed journals up to

March, 2018. They have been sourced from Web of Science, PubMed, IEEE Xplore, and

Science Direct.

This paper has identified the increased sensitivity of some of the techniques used and the

reduction of false positives when some particular algorithms are used. If the medical

community uses these techniques, they can scan more people quickly and thus, taking the

health care to a wider section of the society. This would benefit nation as a whole since early

detection and diagnosis of lung cancer can save potentially millions of people and extend

their life span. This would in turn, generate precious human resource which would be a great

asset to any nation.

In this regard, there should be a close correlation between the medical professionals and the

engineers who develop various CADe and CADx systems. There should be a proper synergy

between various agencies who are at stake in this process. The authors sincerely believe that

this effort would be a right step in that direction.

References

1. American Cancer Society, “Global Cancer Facts and Figures, 2nd

Edition”, (2011)

Atlanta.

2. “Population Fact Sheets: World”, International Agency for Research on Cancer,

World Health Organization (http://gco.iarc.fr/today/fact-sheets-

populations?population=900&sex=0) (2018).

3. “Population Fact Sheets: Asia and India”, International Agency for Research on

Cancer, World Health Organization, (2018).

(http://gco.iarc.fr/today/fact-sheets-populations?population=967&sex=0#collapse0)

(http://gco.iarc.fr/today/fact-sheets-populations?population=356&sex=0#collapse1).

4. D.R.Baldwin, “Prediction of risk of lung cancer in populations and in pulmonary

nodules: Significant progress to drive changes in paradigms”, Lung Cancer, 89 (1)

(2015), 1-3.

5. D.M.Hansell, A.A.Bankier, H.MacMohan, T.C.McLoud, N.L.Muller, J.Remy,

“Fleischner Society: Glossary of terms for thoracic imaging”, Radiology, 246 (3)

(2008), 697-722.


2762

11

6. S.L.A.Lee, A.Z.Kousani, E.J.Ju, “Automated Detection of lung nodules in computed

tomography images: A review”, Machine Vision and Applications, 23(1) (2012), 151-

163.

7. Macedo Firmino, Antonio H. Morais, Roberto M.Mendoca, Marcel R.Dantas,

HelioR.Hekis, Ricardo Valentim, “Computer Aided Detection system for lung cancer

in computed tomography scans: Review and Future Prospects”, Biomedical

Engineering Online, 13 (2014), 1-16.

8. C.I.Henschke, D.I.McCauley, D.F.Yankelevitz, D.P.Naidich, G.McGuinness,

O.S.Miettinen, D.M.Libby, M.W.Pasmantier, J.Koizumi, N.K.Altorki, J.P.smith,

“Early Lung Cancer action Project: Overall design and findings from baseline

screening”, Lancet, 354 (9173) (1999), 99-105.

9. M.Revel, A.Bissery, M.Bienvenu, L.Aycard, C.Lefort, G.Frija, “Are two dimensional

CT measurements of small non-calcified pulmonary nodules reliable?”, Radiology,

231(2) (2004), 453-458.

10. D.L.Renfrew, E.A.Franken, K.S.Berbaum, F.H.Weigelt, M.M.Abu-Yousef, “Error in

Radiology: Classification and lessons in 182 cases presented at a problem case

conference”, Radiology, 183 (1) (1992), 145-150.

11. M.Gomathi, P.Thangaraj, “Computer Aided Medical Diagnosis System for Detection

of Lung Cancer Nodules”, International Journal of Computational Intelligence

Research, 5 (4) (2009), 453-467.

12. A.M.Santos, A.O. de Carvalho Filho, A.C.Silva, A.C. de Paiva, R.A. Nunes,

M.Gattass, “Automatic detection of small lung nodules in 3D CT data using Gaussian

mixture models”, Tsallis Entropy and SVM, Engineering Applications of Artificial

Intelligence, 36 (2014), 27-39.

13. I.R.S.Valente, P.C.Cortez, E.C.Neto, J.M.Soares, V.H. De Albuquerque, J.M.Tavares,

“Automatic 3D pulmonary nodule detection in CT images: A survey”, Computer

methods and programs in biomedicine, 124 (2016), 91-107.

14. Nicholas Petrick, Berkman Sahiner, Samuel G. Armato III, Alberto Bert, Loredana

Correale, Silvia Delsanto, Matthew T. Freedman, David Fryd, David Gur,

LubomirHadjiiski, ZhiminHuo, Yulei Jiang, Lia Morra, Sophie Paquerault, Vikas

Raykar, Frank Samuelson, Ronald M. Summers, Georgia Tourassi, Hiroyuki Yoshida,

Bin Zheng, Chuan Zhou, Heang‐ Ping Chan, “Evaluation of Computer Aided

detection and diagnosis systems”, Medical Physics, 639 (2007) (2013).


2763

12

15. K.Suzuki, “A review of computer aided diagnosis in thoracic and colonic imaging”,

Quantitative imaging in medicine and surgery, 2 (3) (2012), 163-176.

16. L.H.Eadie, P.Taylor, A.P.Gibson, “A systematic review of computer assisted

diagnosis in diagnostic cancer imaging”, European Journal of Radiology, 81 (1)

(2012), 70-76.

17. Ayman El-Baz, Ahmed Elnakib, Mohamed Abou El-Ghar, Georgy

Gimel'farb, Robertalk,Aly Farag, “Automatic detection of 2D and 3D lung nodules in

chest spiral CT scans”, International Journal of Biomedical Imaging, 2013 (2013).

18. Samuel G. Armato III, Maryellen L. Giger, Catherine J. Moran, James T. Blackburn,

Kunio Doi and Heber MacMahon, “Computerized detection of pulmonary nodules on

CT scans”, Radiographics, 19(5) (1999), 1303-1311.

19. Yongbum Lee, Takeshi Hara, Hiroshi Fujita, Shigeki Itoh and Takeo Ishigaki,

“Automated detection of pulmonary nodules in helical CT images based on an

improved template matching technique”, IEEE Transactions on Medical Imaging,

20(7) (2001), 595-604.

20. Tomokazu Oda, Mitsuru Kubo, YoshikiKawata, Noboru Niki, Kenji Eguchi,

Hironobu Ohmatsu, Ryutaro Kakinuma, Mashiro Kaneko, MashikoKusomoto,

Noriyuki Moriyama, Kiyoshi Mori and Hiroyuki Nishiyama, “A detection algorithm

of lung cancer candidate nodules on multislice CT images”, Medical Imaging,

Proceedings of SPIE, 4684 (2002), 1355-1361.

21. Mitsuru Kubo, Kazunori Kubota, Nobuhiro Yamada, YoshikiKawata, Noboru Niki,

Kenji Eguchi, Hironobu Ohmatsu, Ryutaro Kakinuma, Masahiro Kaneko, Masahiko

Kusumoto, Kiyoshi Mori, Hiroyuki Nishiyama and Noriyuki Moriyama, “A CAD

system for lung cancer based on low dose single slice CT image”, Medical Imaging,

Proceedings of SPIE, 4684 (2002), 1262-1269.

22. Samuel G. ArmatoIII, Feng Li, Maryellan L. Giger, Heber MacMohan, Shusuke Sone

and Kunio Doi, “Lung cancer: Performance of automated lung nodule detection

applied to cancers missed in a CT screening program”, Radiology, 225 (2002), 685-

692.

23. Kenji Suzuki, Samuel G. Arshemato, Feng Li, Shusuke Sone and Kunio Doi,

“Massive training artificial neural network(MTANN) for reduction of false positives

in computerized detection of lung nodules in low-dose computed tomography”,

Medical Physics, 30 (7) (2003), 1602-1617


2764

13

24. Qiang Li, Shusuke Sone and Kunio Doi, “Selective enhancement filters for nodules,

vessels and airway walls in two and three-dimensional CT scans”, Medical Physics,

30 (8) (2003), 2040-2051.

25. William J. Kostis, Anthony P. Reeves, David F. Yankelevitz and Claudi I.

Henshke, ”Three dimensional segmentation and growth rate estimation of small

pulmonary nodules in helical CT images”, IEEE Transactions on Medical Imaging,

22 (10) (2003), 1259-1274.

26. David S. Paik, Christopher F. Beaulieu, Geoffrey D. Rubin, BurakAcar, R. Brooke

Jeffrey, Jr., Judy Yee, Joyoni Dey and Sandy Napel, “Surface normal overlap: A

computer aided detection algorithm with application to colonic polyps and lung

nodules in helical CT”, IEEE Transactions on Medical Imaging, 23 (6) (2004), 661-

675.

27. Hidetaka Arimura, ShigehikoKatsuragawa, Kenji Suzuki, Feng Li, JunjiShiraishi,

Shusuke Sone and Kunio Doi, “Computerized scheme for automated detection of lung

nodules in low dose computed tomography images for lung cancer screening”,

Academic Radiology, 11 (2004), 617-629.

28. Aly Farag, Ayman El-Baz, Georgy G. Gimel’farb, Robert Falk and Stephen G.

Hushek, “Automatic detection and recognition of lung Yeabnormalities in helical CT

images using deformable templates”, Medical Image Computing and Computer

Assisted Intervention- MICCAI, Springer, Berlin, LNCS, 3217 (2004), 856-864.

29. Sukmoon Chang, HiroshEmoto, Dimitris N. Metaxas and Leon Axel, “Pulmonary

micronodule detection from 3D chest CT”, MICCAI, Springer, Berlin, LNCS, 3217

(2004), 821-828.

30. Kenji Suzuki, Feng Li, Shusuke Sone and Kunio Doi, “Computer aided diagnostic

scheme for distinction between benign and malignant nodules in thoracic low dose CT

by use of massive training artificial neural network”, IEEE Transactions on Medical

Imaging, 24 (9) (2005), 1138-1150.

31. Paulo R.S. Mendonca, Rahul Bhotika, Sadd A. Sirohey, Wesley D. Turner, James V.

Miller and Ricardo S. Avila, “Model-Based Analysis of local shape for lesion

detection in CT scans”, MICCAI 2005, Springer, Berlin, LNCS, 3749 (2005), 688-695.

32. Kyongtae T. Bae, Jin-Sung Kim, Yong-Hum Na, Kwang Gi Kim and Jin-Hwan Kim,

“Pulmonary Nodules: Automated detection on CT images with morphologic matching

algorithm-preliminary results”, Radiology, 236 (2005), 286-294.


2765

14

33. Jan-Martin Kuhnigk, Volker Dicken, Lars Bornemann, Annemarie Bakai, Dag

Wormanns, Stefan Krass and Heinz-Otto Peitgen, “Morphological segmentation and

partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT

scans”, IEEE Transactions on Medical Imaging, 25 (4) (2006), 417-434.

34. Jinghao Zhou, Sukmoon Chang, Dimitris N. Metaxas, Binsheng Zhao, Michelle S.

Ginsberg and Lawrence H. Schwartz, “An automatic method for ground glass opacity

nodule detection and segmentation from CT studies”, Proceedings of the 28th

IEEE

EMBS Annual International Conference, (2006), 3062-3065.

35. Xiangwei Zhang, Jonathan Stockel, Matthias Wolf, Pascal Cathier, Geoffrey

McLennan, Eric A. Hoffman and Milan Sonka, “A new method for spherical object

detection and its application to computer aided detection of pulmonary nodules in CT

images”, MICCAI 2007, Springer, Berlin, LNCS, 4791, (2007), 842-849.

36. R. Bellotti, F. De Carlo, G. Gargano, D. Cascio, E Catanzariti, Bruno Golosio, “A

CAD system for nodule detection in low-dose lung CTs based on region growing and

a new active contour model”, Medical Physics, 34 (12) (2007), 4901-4910.

37. Stefano Diciotti, Giulia Picozzi, Massimo Falchini, Mario Mascalchi, Natale Villari

and Guido Valli, “3D segmentation algorithm of small lung nodules in spiral CT

images”, IEEE Transactions on Information Technology in Biomedicine, 12 (1),

(2008), 7-19.

38. Jamshid Dehmeshki, Hamdan Amin, ManlioValdivieso and Xujiong Ye,

“Segmentation of pulmonary nodules in thoracic CT scnas: A region growing

approach”, IEEE Transactions on Medical Imaging, 27 (4) (2008), 467-480.

39. SerhatOzekes, Onur Osman and Osman N. Ucan, “Nodule detection in a lung region

that’s segmented with using genetic cellular neural networks and 3D template

matching with fuzzy rule based thresholding”, Korean Journal of Radiology, 9 (1)

(2008), 1-9.

40. Jiantao Pu, Justus Roos, Chin A. Yi, Sandy Napel, Geoffrey D. Rubin, David S. Paik,

“Adaptive border marching algorithm: Automatic lung segmentation on chest CT

images”, Computerized Medical imaging and Graphics, 32 (2008), 452-462.

41. Qiang Li, Feng Li and Kunio Doi, “Computerized detection of lung nodules in thin

section CT images by use of selective enhancement filters and an automated rule-

based classifier”, Academic Radiology, 15(2) (2008), 165-175.


2766

15

42. Xujiong Ye, Xinju Lin, Jamshid Dehmeshki, “Shape based computer aided detection

of lung nodules in thoracic CT images”, IEEE Transactions on Biomedical

Engineering, 56 (7) (2009), 1810-1820.

43. Jorge Juan Suarez-Cuenca, Pablo G. Tahoces, Miguel Souto, Maria J. Lado, Martine

Remy-Jardin, Jacques Remy and Juan Jose Vidal, “Application of the iris filter for

automatic detection of pulmonary nodules on computed tomography images”,

Computers in Biology and Medicine, 39 (2009), 921-933.

44. TemesguenMessay, Russell C. Hardie, Steven K. Rogers, “A new computationally

efficient CAD system for pulmonary nodule detection in CT imagery”, Medical

Image Analysis, 14 (2010), 390-406

45. S.L.A. Lee, A.Z. Kouzani, E.J. Hu, “Random forest based lung nodule classification

aided by clustering”, Computerized Medical Imaging and Graphics, 34 (2010), 535-

542.

46. Joao Rodrigo Ferreira da Silva Sousa, Aristofanes Correa Silva, Anselmo Cardoso de

Paiva, RudolfoAcatauassu Nunes, “Methodology for automatic detection of lung

nodules in computerized tomography images”, Computer Methods and Programs in

Biomedicine, 98 (2010), 1-14.

47. Jiantao Pu, David S. Paik, Xin Meng, Justus E. Roos and Geoffrey D. Rubin, “Shape

break and repair strategy and its applications to automated medical image

segmentation”, IEEE Transactions on Visualization and Computer Graphics, 17 (1)

(2011), 115-124.

48. Toshiro Kubota, Anna K. Jerebko, Maneesh Dewan, Marcos Salganicoff and Arun

Krishnan, “Segmentation of pulmonary nodules of various densities with

morphological approaches and convexity models”, Medical Image Analysis, 15

(2011), 133-154.

49. Stefano Diciotti, Simone Lombardo, Massimo Falchini, Giulia Picozzi and Mario

Mascalchi, “Automated segmentation refinement of small lung nodules in CT scans

by local shape analysis”, IEEE Transactions on Biomedical Engineering, 58 (12)

(2011), 3418-3428.

50. Shanhui Sun, Christian Bauer, Reinhard Beichel, “Automated 3-D segmentation of

lungs with lung cancer in CT data using a novel robust active shape model approach”,

IEEE Transactions on Medical Imaging, 31 (2) (2012), 449-459


2767

16

51. D.S. Elizabeth, H. K. Nehemiah, C.S. Retmin Raj and A. Kannan, “Computer aided

diagnosis of lung cancer based on analysis of the significant slice of chest computed

tomography image”, IET Image Processing, 6 (6) (2012), 697-705.

52. StelmoMagalhaes Barros Netto, Aristofanes Correa Silva, Rodolfo Acatauassu Nunes,

Marcelo Gattass, “Automatic segmentation of lung nodules with growing neural gas

and support vector machine”, Computers in Biology and Medicine, 42 (2012), 1110-

1121.

53. S. Sivakumar, C. Chandrasekar, “Lung nodule segmentation through unsupervised

clustering models”, International Conference on Modelling, Optimization and

Computing (ICMOC2012), Procedia Engineering, 38 (2012), 3064-3073.

54. Wook-Jin Choi and Tae-Sun Choi, “Automated pulmonary nodule detection system in

computed tomography images: A hierarchical block classification approach”, Entropy,

15 (2013), 507-523.

55. Amal A. Farang, James H. Graham, “A novel approach for lung nodules segmentation

in chest CT using level sets”, IEEE Transactions on Image Processing, 22 (12) (2013),

5202-5212.

56. Jinsa Kuruvilla, K. Gunavathi, “Lung Cancer classification using neural networks for

CT images”, Computer Methods of Programs in Biomedicine, 113 (2014), 202-209

57. Colin Jacobs, Eva M. van Rikxoort, Thorsten Twellmann, Ernt Th. Scholten, Pim A.

de Jong, Jan-Martin Kuhnigk, Matthijs Ouderk, Harry J. de Koning and Mathias

Prokop, “Automatic detection of subsolid pulmonary nodules in thoracic computed

tomography images”, Medical Image Analysis, 18 (2014), 374-384.

58. Tong Jia, Hao Zhang and Haixiu Meng, “A novel lung nodules detection scheme

based on vessel segmentation on CT images”, Bio-Medical Materials and

Engineering, 24 (2014), 3179-3186.

59. Wook-Jin Choi, Tae-Sun Choi, “Automated Pulmonary nodule detection based on

three-dimensional shape based feature descriptor”, Computer Methods and Program

in Biomedicine, 113 (2014), 37-54

60. Shengjun Zhou, Yuanzhi Cheng and Shinichi Tamura, “Automated lung segmentation

and smoothing techniques for inclusion of juxtapleural nodules and pulmonary vessels

on chest CT images”, Biomedical Signal Processing and Control, 13 (2014), 62-70.

61. Hao Han, Lihong Li, Fangfang Han, Bowen Song, William Moore and Zhengrong

Liang, “Fast and adaptive detection of pulmonary nodules in thoracic CT images


2768

17

using a hierarchical vector quantization scheme”, IEEE Journal of Biomedical and

Health Informatics, 19 (2) (2015), 648-659.

62. TemesguenMessay, Russell C. Hardie and Timothy R. Tuinstra, “Segmentation of

pulmonary nodules in computed tomography using a regression neural network

approach and its application to the lung image database consortium and image

database resource initiative”, Medical Image Analysis, 22 (2015), 48-62.

63. Sudipta Mukhopadhyay, “A segmentation framework of pulmonary nodules in lung

CT images”, Journal of Digital Imaging, 29 (1) (2016), 86-103.

64. Jibi John, M.G. Mini, “Multilevel Thresholding Based Segmentation and Feature

Extraction for Pulmonary Nodule Detection”, International Conference on Emerging

Trends in Engineering, Science and Technology (ICETEST-2015), Procedia

Technology, 24 (2016), 957-963

65. Ashis Kumar Dhara, Sudipta Mukhopadhyay, Anirvan Dutta, Mandeep Garg and

Niranjan Khandelwal, “A combination of shape and texture features for classification

of pulmonary nodules in lung CT images”, Journal of Digital Imaging, 29 (4) (2016),

466-475.

66. Arnaud A. A. Setio, Francesco Ciompi, Geert Litjens, Paul Gerke, Colin Jacobs,

Sarah J. van Riel, Mathilde Marie Winkler Wille, MatiullahNaqibullah, Clara I.

Sanchez, Bram van Ginneken, “Pulmonary nodule detection in CT images: false

positive reduction using multi-view convolutional networks”, IEEE Transactions on

Medical Imaging, 35 (5), (2016), 1160-1169.

67. Tao Zhou, Huiling Lu, Junjie Zhang and Hongbin Shi, “Pulmonary nodule detection

model based on SVM and CT image feature level fusion with rough sets”, BioMed

Research International Article ID 8052436, (2016), 1-13.

68. MuzzamilJavaid, Moazzam Javid, Muhammad Zia Ur Rehman, Syed Irtiza Ali Shah,

“A novel approach to CAD system for the detection of lung nodules in CT images”,

Computer methods and programs in biomedicine, 135 (2016), 125-139.

69. Ezhil E. Nithila, S.S. Kumar, “Automatic detection of solitary pulmonary nodules

using swarm intelligence optimized neural networks on CT images”, Engineering

Science and Technology, 20 (2017), 1192-1202.

70. Ji-kui Liu, Hong-yang Jiang, Meng-di Gao, Chen-guang He, Yu Wang, Pu Wang, He

Ma and Ye li, “An assisted diagnosis system for detection of early pulmonary nodule

in computed tomography images”, Journal of Medical Systems, 41 (2) (2017), 30.


2769

18

71. Qaisar Abbas, “Segmentation of differential structures on computed tomography

images for diagnosis lung related diseases”, Biomedical Signal Processing and

Control, 33 (2017), 325-334.

72. Wei Shen, Mu Zhou, Feng Yang, Dongdong Yu, Di Dong, Caiyun Yang, Yali Zang,

Jie Tian, “Multi-crop Convolutional Neural Networks for lung nodule malignancy

suspiciousness classification”, Pattern Recognition, 61 (2017), 663-673.

73. Qi Dou, Hao Chen, Lequan Yu, Jing Qin and Pheng-Ann Heng (2017), “Multi-level

contextual 3D CNNs for false positive reduction in pulmonary nodule detection”,

IEEE Transactions on Biomedical Engineering 64 (7), 1558-1567.

74. Jose Raniery Ferreira Junior, Marcel Koenigkam-Santos, Federico Enrique Garcia

Cipriano, Alexandre TodorovicFabro and Paulo Mazzoncini de Azevedo-Marques,

“Radiomics based features for pattern recognition of lung cancer histopathology and

metastases”, Computer Methods and Programs in Biomedicine, 159 (2018), 23-30.

75. Xie Yutong, Zhang Jianpeng, Xia Yong, Michael Fulham and ZhangYanning,

“Fusing texture, shape and deep model learned information at decision level for

automated classification of lung nodules on chest CT”, Information Fusion, 42 (2018),

102-110.

76. Farzad VasheghaniFarahani, Abbas Ahmadi and Mohammad Hossein Fazel Zarandi,

“Hybrid intelligent approach for diagnosis of the lung nodule from CT images using

spatial kernelized fuzzy c-means and ensemble learning”, Mathematics and

Computers in Simulation, 149 (2018), 48-68.

77. Jing Gong, Ji-yu Liu, LI-jia Wang, Xi-wen Sun, Bin Zheng, Sheng-dong Nie,

“Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor

filtering with local image feature analysis”, Physic Medica, 46 (2018), 124 -133.

78. Dr. Divya Midhunchakkaravarthy,” An Efficient And Secure Detection Of Internet

Worm Using Propagation Model”, International Journal Of Innovations In Scientific

And Engineering Research, Vol .3, Issue .1,(2016), 8-15.


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