Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography

Author(s): Shabana Rasheed Ziyad*, Venkatachalam Radha and Thavavel Vayyapuri

Volume 16, Issue 1, 2020

Page: [16 - 26] Pages: 11

DOI: 10.2174/1573405615666190206153321

Price: $65

Abstract

Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules.

Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx.

Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules.

Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper.

Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

Keywords: Lung cancer, lung nodules, computed tomography, noise removal, lung segmentation, nodule classification.

Graphical Abstract

[1]
World Health Organization (WHO) 2017. Available from:. 2017.http://www.who.int/mediacentre/factsheets/fs297/en/
[2]
Web MD. Lung cancer causes and risks 2017 Available from: http://www.webmd.com/lung-cancer/lung-cancer-causes#1
[3]
Low-dose CT scan for lung cancer screening; 2017. Available from: http://www.swedish.org/services/thoracic-surgery/our-services/lung-cancer-screening-program/low-dose-ct-scan-for-lung-cancer-screening
[4]
Liu Q, Liu J, Xiong B, Liang D. A non-convex gradient fidelity-based variational model for image contrast enhancement. EURASIP J Adv Signal Process 2014; 2014: 154.
[http://dx.doi.org/10.1186/1687-6180-2014-154]
[5]
Sprawls P. AAPM tutorial. CT image detail and noise. Radiographics 1992; 12(5): 1041-6.
[http://dx.doi.org/10.1148/radiographics.12.5.1529128] [PMID: 1529128]
[6]
Sprawls P. Computed tomography image quality optimization and dose management Available from: http://www.sprawls.org/resources/CTIQDM/#4
[7]
Edward BF, Fleischmann D. CT artifacts, causes and reduction techniques. Imaging Med 2012; 4(2): 229-40.
[http://dx.doi.org/10.2217/iim.12.13]
[8]
Zhao W, Vernekohl D, Zhu J, Wang L, Xing L. A model-based scatter artifacts correction for cone beam CT. Med Phys 2016; 43(4): 1736-53.
[http://dx.doi.org/10.1118/1.4943796] [PMID: 27036571]
[9]
Gravel P, Beaudoin G, De Guise JA. A method for modeling noise in medical images. IEEE Trans Med Imaging 2004; 23(10): 1221-32.
[http://dx.doi.org/10.1109/TMI.2004.832656] [PMID: 15493690]
[10]
Lei T, Sewchand W. Statistical approach to X-ray CT imaging and its applications in image analysis. I. Statistical analysis of X-ray CT imaging. IEEE Trans Med Imaging 1992; 11(1): 53-61.
[http://dx.doi.org/10.1109/42.126910] [PMID: 18218356]
[11]
Dai S, Lu K, Dong J, Zhang Y, Chen Y. A novel approach of lung segmentation on chest CT images using graph cuts. Neurocomputing 2015; 168: 799-807.
[http://dx.doi.org/10.1016/j.neucom.2015.05.044]
[12]
Camarlinghi N. Automatic detection of lung nodules in computed tomography images-training and validation of algorithms using public research database. Eur Phys J Plus 2013; 128: 110.
[http://dx.doi.org/10.1140/epjp/i2013-13110-5]
[13]
Choi WJ, Choi TS. Automated pulmonary nodule detection system in computed tomography images-a hierarchical block classification approach. Entropy (Basel) 2013; 15: 507-23.
[http://dx.doi.org/10.3390/e15020507]
[14]
Disha S, Gagandeep J. Identifying lung cancer using image processing techniques. Int Conf Comp Tech Artif Intell 2011; 3(5): 714-8.
[15]
Kumar M, Diwakar M. CT image denoising using locally adaptive shrinkage rule in tetrolet domain. JKSUS-Comp Inform Sci 2018; 30(1): 41-50.
[http://dx.doi.org/10.1016/j.jksuci.2016.03.003]
[16]
Green M, Marom EM, Konen E, Kiryati N, Mayer A. Patient-specific image denoising for ultra-low-dose CT-guided lung biopsies. Int J CARS 2017; 12(12): 2145-55.
[http://dx.doi.org/10.1007/s11548-017-1621-6] [PMID: 28601962]
[17]
Al-amri Salem Saleh. Kalyankar N V, Khamitkar S D. Linear and non-linear contrast enhancement image. IJCSNS 2010; 10(2): 139-43.
[18]
Javaid M, Javid M, Rehman MZ, Shah SI. A novel approach to CAD system for the detection of lung nodules in CT images. Comput Methods Programs Biomed 2016; 135: 125-39.
[http://dx.doi.org/10.1016/j.cmpb.2016.07.031] [PMID: 27586486]
[19]
Netto SMB, Silva AC, Nunes RA, Gattass M. Automatic segmentation of lung nodules with growing neural gas and support vector machine. Comput Biol Med 2012; 42(11): 1110-21.
[http://dx.doi.org/10.1016/j.compbiomed.2012.09.003]
[20]
Prasada RBS, Rajesh K, Rajesh KP. Contrast enhancement of low dose CT scan images. IJCTA 2015; 8(5): 2415-22.
[21]
Wan ZWI, Sim KS. Contrast enhancement dynamic histogram equalization for medical image processing. Appl Int J Imaging Syst Technol 2011; 21(3): 280-9.
[http://dx.doi.org/10.1002/ima.20295]
[22]
Jin Y, Fayad L, Laine A. Contrast enhancement by multi-scale adaptive histogram equalization wavelets. SPIE: San Diego 2001.
[23]
Sandeep A. Dwivedi, Borse RP, Anil Yametkar M. Lung cancer detection and classification by using machine learning & multinomial Bayesian. IOSR J Elect Comm Eng 2014; 9(1): 69-75.
[http://dx.doi.org/10.9790/2834-09136975]
[24]
Al-Ameen Z, Sulong G, Rehman A, Al-Dhelaan A, Saba T, Al-Rodhaan M. An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization. EURASIP J Adv Signal Process 2015; 2015: 32.
[http://dx.doi.org/10.1186/s13634-015-0214-1]
[25]
Kumar KR, Sinha GR, Kumar S. Fuzzy based contrast Enhancement method for lung cancer CT images. IJECS 2017; 6(5): 21201-4.
[26]
John J, Mini MG. Multilevel thresholding based segmentation and feature extraction for pulmonary nodule detection. Procedia Technol 2016; 24: 957-63.
[27]
Cascio D, Magro R, Fauci F, Iacomi M, Raso G. Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models. Comput Biol Med 2012; 42(11): 1098-109.
[http://dx.doi.org/10.1016/j.compbiomed.2012.09.002] [PMID: 23020972]
[28]
Jang H-S, Choi W-J, Choi T-S. Optimal fuzzy rule based pulmonary nodule detection. Adv Sci Technol Lett 2013; 29: 75-8.
[29]
Iqbal S, Iqbal K, Arif F, Shaukat A, Khanum A. Potential lung nodules identification for characterization by variable multistep threshold and shape indices from CT images. Comput Math Methods Med 2014; 2014: 1-7.
[http://dx.doi.org/10.1155/2014/241647] [PMID: 25506388]
[30]
Than M, Chia J, Noor NM, Rijal OM, Yunus A. Kassim, Lung segmentation for HRCT thorax images using radon transform and accumulating pixel width.Proceedings of the IEEE Region 10 Symposium. IEEE; Kuala Lumpur, Malaysia 2014; pp. 157-61.
[31]
Orozco HM, Osslan OVV, Vianey GCS, Humberto de Jesús OD, Manuel de Jesús NA. Automated system for lung nodule classification based on wavelet feature descriptor and support vector machine. Biomed Eng Online 2015; 14(9): 1-20.
[http://dx.doi.org/10.1186/s12938-015-0003-y]
[32]
Shen S, Bui AA, Cong J, Hsu W. An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Comput Biol Med 2015; 57: 139-49.
[http://dx.doi.org/10.1016/j.compbiomed.2014.12.008] [PMID: 25557199]
[33]
Manikandan T, Bharathi N. Lung cancer detection using fuzzy auto-seed cluster means morphological segmentation and SVM classifier. J Med Syst 2016; 40(7): 181.
[http://dx.doi.org/10.1007/s10916-016-0539-9] [PMID: 27299354]
[34]
Gong J, Liu JY, Wang LJ, Zheng B, Nie SD. Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier. Phys Med 2016; 32(12): 1502-9.
[http://dx.doi.org/10.1016/j.ejmp.2016.11.001] [PMID: 27856118]
[35]
Shaukat F, Raja G, Gooya A, Frangi AF. Fully automatic detection of lung nodules in CT images using a hybrid feature set. Med Phys 2017; 44(7): 3615-29.
[http://dx.doi.org/10.1002/mp.12273] [PMID: 28409834]
[36]
Nithya E, Kumar S. Segmentation of lung nodule in CT data using active contour model and fuzzy C means clustering. Alexandria Eng J 2016; 55: 2583-8.
[http://dx.doi.org/10.1016/j.aej.2016.06.002]
[37]
Liu H, Geng F, Guo Q, Zhang C, Zhang C. A fast weak-supervised pulmonary nodule segmentation method based on modified self-adaptive FCM algorithm. Soft Comput 2017; 12(22): 3983-95.
[38]
von Berg J, Young S, Carolus H, et al. A novel bone suppression method that improves lung nodule detection: suppressing dedicated bone shadows in radiographs while preserving the remaining signal. Int J CARS 2016; 11(4): 641-55.
[http://dx.doi.org/10.1007/s11548-015-1278-y] [PMID: 26337439]
[39]
Saien S, Hamid Pilevar A, Abrishami Moghaddam H. Refinement of lung nodule candidates based on local geometric shape analysis and Laplacian of Gaussian kernels. Comput Biol Med 2014; 54: 188-98.
[http://dx.doi.org/10.1016/j.compbiomed.2014.09.010] [PMID: 25303113]
[40]
Mansoor A, Bagci U, Xu Z, et al. A generic approach to pathological lung segmentation. IEEE Trans Med Imaging 2014; 33(12): 2293-310.
[http://dx.doi.org/10.1109/TMI.2014.2337057] [PMID: 25020069]
[41]
Krishnamurthy SK, Narasimhan G, Rengasamy U. Three-dimensional lung nodule segmentation and shape variance analysis to detect lung cancer with reduced false positives. J Eng Med 2016; 230(1): 58-70.
[http://dx.doi.org/10.1177/0954411915619951]
[42]
Mukhopadhyay S. A Segmentation framework of pulmonary nodules in lung CT images. J Digit Imaging 2016; 29(1): 86-103.
[http://dx.doi.org/10.1007/s10278-015-9801-9] [PMID: 26055544]
[43]
Liu X, Hou F, Qin H, Hao A. A CADe system for nodule detection in thoracic CT images based on artificial neural network. Sci China Inf Sci 2016; 60(7): 1-15.
[44]
Arfan JM, Basit SA, Mubashar M. Ensemble classification of pulmonary nodules using gradient intensity feature descriptor and differential evolution. Cluster Comput 2018; 21(1): 393-407.
[45]
Kumar D, Wong A, Clausi DA. Lung nodule classification using deep features in CT images.Proceedings of 12th Conference on Computer and Robot Vision (CRV). IEEE; Waterloo, Canada; 2015; pp. 133-8.
[http://dx.doi.org/10.1109/CRV.2015.25]
[46]
Liu Y, Wang Z, Guo M, Li P. Hidden conditional random field for lung nodule detection.Proceedings of the IEEE International Conference on Image Processing (ICIP). IEEE; Paris, France; 2014; pp. 3518-21.
[http://dx.doi.org/10.1109/ICIP.2014.7025714]
[47]
Rahman MM, You D, Simpso MS, Sameer AK, Fushman DD, George TR. Interactive cross and multimodal biomedical image retrieval based on automatic region-of-interest (ROI) identification and classification. Int J Multimed Inf Retr 2014; 3(3): 131-46.
[http://dx.doi.org/10.1007/s13735-014-0057-9]
[48]
Darmanayagam SE, Harichandran KN, Cyril SR, Arputharaj K. A novel supervised approach for segmentation of lung parenchyma from chest CT for computer-aided diagnosis. J Digit Imaging 2013; 26(3): 496-509.
[http://dx.doi.org/10.1007/s10278-012-9539-6] [PMID: 23076539]
[49]
Filho AOC, Silva AC, de Paiva AC, Nunes RA, Gattass M. Lung nodule classification based on computed tomography using taxonomic diversity indexes and an SVM. J Sign Process Syst 2017; 87: 179-96.
[http://dx.doi.org/10.1007/s11265-016-1134-5]
[50]
Froz BR, Filho AOC, Silva AC, Paiva AC, Nunes RA, Gattass M. Lung nodule classification using artificial crawlers, directional texture and support vector machine. Expert Syst Appl 2017; 69: 176-88.
[http://dx.doi.org/10.1016/j.eswa.2016.10.039]
[51]
Cao P, Liu X, Yang J, Zhao D, Li W, et al. A multi-kernel based framework for heterogeneous feature selection and over-sampling for computer-aided detection of pulmonary nodules. Pattern Recognit 2017; 2017(64): 327-46.
[http://dx.doi.org/10.1016/j.patcog.2016.11.007]
[52]
Dhara AK, Mukhopadhyay S, Dutta A, Garg M, Khandelwal N. A Combination of shape and texture features for classification of pulmonary nodules in lung CT images. J Digit Imaging 2016; 29(4): 466-75.
[http://dx.doi.org/10.1007/s10278-015-9857-6] [PMID: 26738871]
[53]
Nibali A, He Z, Wollersheim D. Pulmonary nodule classification with deep residual networks. Int J CARS 2017; 12(10): 1799-808.
[http://dx.doi.org/10.1007/s11548-017-1605-6] [PMID: 28501942]
[54]
Yuan J, Liu X, Hou F, Qin H, Hao A. Hybrid-feature-guided lung nodule type classification on CT images. Comput Graph 2017; 000: 1-12.
[55]
Aswathy VG, Johncy Rani T. A supervised lung nodule classification method using patch based context analysis in LDCT Image. Int J Eng Res Gen Sci 2015; 3(1): 928-37.
[56]
Gonçalves L, Novo J, Campilho A. Hessian based approaches for 3D lung nodule segmentation. Expert Syst Appl 2016; 2016(61): 1-15.
[http://dx.doi.org/10.1016/j.eswa.2016.05.024]
[57]
Filho AOC, Silva AC, de Paiva AC, Nunes RA, Gattass M. 3D shape analysis to reduce false positives for lung nodule detection systems. Med Biol Eng Comput 2017; 55: 1199-213.
[http://dx.doi.org/10.1007/s11517-016-1582-x]
[58]
Chen B, Kitasaka T, Honma H, et al. Automatic segmentation of pulmonary blood vessels and nodules based on local intensity structure analysis and surface propagation in 3D chest CT images. Int J CARS 2012; 7(3): 465-82.
[http://dx.doi.org/10.1007/s11548-011-0638-5] [PMID: 21739111]
[59]
Kitasaka T, Mori K, Hasegawa J, Toriwaki J. Lung area extraction from 3-D chest x-ray CT images using the shape model generated by variable bezier surface. Sys Comp Japan Bann 2003; 34(4): 60-71.
[http://dx.doi.org/10.1002/scj.1201]

Rights & Permissions Print Cite
Article Metrics
48
2
© 2024 Bentham Science Publishers | Privacy Policy