Image Classifiers in Endoscopy for Detection of Malignancy in Gastro Intestinal Tract

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K V Mahendra Prashanth 1,* Vani V 1

1. SJB Institute of Technology,Visvesvaraya Technological University, Bangalore,India

* Corresponding author.


Received: 24 Feb. 2017 / Revised: 4 Apr. 2017 / Accepted: 9 May 2017 / Published: 8 Jun. 2017

Index Terms

Image classification, Wireless Capsule Endoscopy (WCE), Machine Learning, Support Vector Machine (SVM)


Wireless Capsule Endoscopy (WCE) is one of the methods for examination of gastrointestinal (GI) disorders such as obscure GI bleeding, Crohns disease, polyps etc. WCE has been recognized as a less expensive and painless procedure for the diagnosis of GI tract. This paper examines the various image classifiers designed and developed for the purpose of endoscopy focusing specifically on WCE. It is revealed that designing a suitable image classifier is an important prerequisite for accurate and precise diagnosis of malignancy in WCE. The assessment on various image classifiers used for the diagnosis of pathologies in different parts of GI tract shows that classifiers have reduced the diagnosis time for medical experts and also provided reasonably accurate diagnosis of malignancy. However, correlating classifiers and related pathologies is still observed to be challenging. In view of the fact that early detection may decrease the mortality rate significantly, inclination towards computer aided diagnosis are expected to increase in future. There is a need for advanced research in the development of a robust computer aided diagnosis system, capable of diagnosis of various pathologies in GI tract with higher degree of accuracy and reliability. Further, the study depicts that a direct comparison of results of classifier such as accuracy, prediction, sensitivity, specificity and precision to evaluate its performance is challenging due to diversity of image databases. More research is needed to identify and reduce the uncertainties in the application of image classifier to improve the diagnosis accuracy.

Cite This Paper

K V Mahendra Prashanth, Vani V,"Image Classifiers in Endoscopy for Detection of Malignancy in Gastro Intestinal Tract", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.6, pp.45-54, 2017. DOI: 10.5815/ijigsp.2017.06.06


[1]G. Iddan, G. Meron, A. Glukhovsky, P. Swain, Nature 405, 417 (2000)

[2]J.D. Mellinger, Surgical Innovation 10(1), 3 (2003)

[3]A. Moglia, A. Menciassi, P. Dario, A. Cuschieri, The Lancet 370(9582), 114 (2007)

[4]V.S. Prasath, R. Delhibabu, in Computational Intelligence in Medical Informatics (Springer, 2015), pp. 73–80

[5]M. Liedlgruber, A. Uhl, Department of Computer Sciences, University of Salzburg, Austria,http://www. cosy. sbg. ac. at/research/tr. html, Tech. Rep 1, 2011 (2011)

[6]S.L. Triester, J.A. Leighton, G.I. Leontiadis, D.E. Fleischer, A.K. Hara, R.I. Heigh, A.D. Shiff, V.K. Sharma, The American journal of gastroenterology 100(11), 2407 (2005)

[7]L. Fisher, M.L. Krinsky, M.A. Anderson, V. Appalaneni, S. Banerjee, T. Ben-Menachem, B.D. Cash, G.A. Decker, R.D. Fanelli, C. Friis, et al., Gastrointestinal endoscopy 72(3), 471 (2010)

[8]B.S. Lewis, P. Swain, Gastrointestinal endoscopy 56(3), 349 (2002)

[9]M. Mylonaki, A. Fritscher-Ravens, P. Swain, Gut 52(8), 1122 (2003)

[10]A. Mata, J. Llach, J. Bordas, F. Feu, M. Pellis´e, G. Fern´andez-Esparrach, A. Gin´es, J. Piqu´e, Gastroenterologia y hepatologia 26(10), 619 (2003)

[11]J. Saurin, M. Delvaux, J. Gaudin, I. Fassler, J. Villarejo, K. Vahedi, A. Bitoun, J. Canard, J. Souquet, T. Ponchon, et al., Endoscopy 35(7), 576 (2003)

[12]M. Mu˜noz-Navas, (2008)

[13]J. Church, Diseases of the Colon & Rectum 51(5), 520 (2008)

[14]T. Kav, Y. Bayraktar, World J Gastroenterol 15(16), 1934 (2009)

[15]E. Rondonotti, F. Villa, C.J. Mulder, M. Jacobs, R. de Franchis, World Journal of Gastroenterology 13(46), 6140 (2007)

[16]J.A. Leighton, V.K. Sharma, K. Srivathsan, R.I. Heigh, T.L. McWane, J.K. Post, S.R.Robinson, J.L. Bazzell, D.E. Fleischer, Gastrointestinal endoscopy 59(4), 567 (2004)

[17]Z. Fireman, E. Mahajna, E. Broide, M. Shapiro, L. Fich, A. Sternberg, Y. Kopelman, E. Scapa, Gut 52(3), 390 (2003)

[18]R. Kumar, T. Dassopoulos, H. Girgis, G. Hager. System and method for automated disease assessment in capsule endoscopy (2010). US Patent App. 13/382,855

[19]J.R. Quinlan, C4. 5: programs for machine learning (Elsevier, 2014)

[20]V. Kodogiannis, M. Boulougoura, in Neural Networks, 2005. IJCNN’05. Proceedings. 2005 IEEE International Joint Conference on, vol. 4 (IEEE, 2005), vol. 4, pp. 2423–2428

[21]B. Sch¨olkopf, C.J. Burges, Advances in kernel methods: support vector learning (MIT press, 1999)

[22]J. Mennicke, C. M¨unzenmayer, T. Wittenberg, U. Schmid, in 4th European Conference of the International Federation for Medical and Biological Engineering (Springer, 2009), pp. 629–632

[23]T.M. Cover, P.E. Hart, Information Theory, IEEE Transactions on 13(1), 21 (1967)

[24]J. Bernal, J. S´anchez, F. Vilarino, Pattern Recognition 45(9), 3166 (2012)

[25]P.N. Figueiredo, I.N. Figueiredo, S. Prasath, R. Tsai, Diagnostic and Therapeutic Endoscopy 2011 (2011)

[26]A. Karargyris, N. Bourbakis, in Life Science Systems and Applications Workshop, 2009. LiSSA 2009. IEEE/NIH (IEEE, 2009), pp. 143–147

[27]A. Karargyris, N. Bourbakis, Biomedical Engineering, IEEE Transactions on 58(10), 2777 (2011)

[28]O. Romain, A. Histace, J. Silva, J. Ayoub, B. Granado, A. Pinna, X. Dray, P.Marteau, in Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on (IEEE, 2013), pp. 1–6

[29]C. Cortes, V. Vapnik, Machine learning 20(3), 273 (1995)

[30]I.N. Figueiredo, S. Prasath, Y.H.R. Tsai, P.N. Figueiredo, ICES REPORT 10, 36 (2010)

[31]Q.Zhao, M.Q.H. Meng, in Intelligent Control and Automation (WCICA), 2011 9th World Congress on (IEEE, 2011), pp. 948–952

[32]S. Hwang, in Advances in Visual Computing (Springer, 2011), pp. 320–327

[33]D.A.R. Vigo, F.S. Khan, J. Van de Weijer, T. Gevers, in Pattern Recognition (ICPR), 2010 20th International Conference on (IEEE, 2010), pp. 1549–1553

[34]M.W. Mackiewicz, M. Fisher, C. Jamieson, in Medical Imaging (International Society for Optics and Photonics, 2008), pp. 69,140R–69,140R

[35]B. Li, M.Q.H. Meng, Computers in biology and medicine 39(2), 141 (2009)

[36]M. Liedlgruber, A. Uhl, Biomedical Engineering, IEEE Reviews in 4, 73 (2011)

[37]Y.S. Jung, Y.H. Kim, D.H. Lee, J.H. Kim, in BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on, vol. 1 (IEEE, 2008), vol. 1, pp. 859–862

[38]S. Hwang, J. Oh, J. Cox, S.J. Tang, H.F. Tibbals, in Medical Imaging (International Society for Optics and Photonics, 2006), pp. 61,441P–61,441P

[39]V.S. Charisis, C. Katsimerou, L.J. Hadjileontiadis, C.N. Liatsos, G.D. Sergiadis, in Computer-Based Medical Systems (CBMS), 2013 IEEE 26th International Symposium on (IEEE, 2013), pp. 203–208

[40]I.S. Reed, X. Yu, Acoustics, Speech and Signal Processing, IEEE Transactions on 38(10), 1760 (1990)

[41]T. Ghosh, S. Bashar, S. Fattah, C. Shahnaz, K. Wahid, in Computer and Information Technology (ICCIT), 2014 17th International Conference on (IEEE, 2014), pp. 354–357

[42]B. Li, M.Q. Meng, in Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on (IEEE, 2009), pp. 498–503

[43]E.J. Ciaccio, C.A. Tennyson, S.K. Lewis, S. Krishnareddy, G. Bhagat, P.H. Green, computer methods and programs in biomedicine 100(1), 39 (2010)

[44]R. Kumar, Q. Zhao, S. Seshamani, G. Mullin, G. Hager, T. Dassopoulos, Biomedical Engineering, IEEE Transactions on 59(2), 355 (2012)

[45]E.B. Valdeavilla, S.G. Miaou, (AIT 2010, 2010)

[46]G. Gallo, A. Torrisi, in The Third International Conferences on Pervasive Patterns and Applications (2011), pp. 25–30

[47]Htwe, L. Shen Weijia, J. Poh Chee Khun, O. Lim Joo Hwee, K.Y. Ho, in Asia PacificSigna l and Information Processing Association Annual Summit and Conference (APSIPA) (APSIPA, 2010), pp. 653–656

[48]L. Igual, J. Vitria, F. Vilarino, S. Segu´ı, C. Malagelada, F. Azpiroz, P. Radeva, in 4th European Conference of the International Federation for Medical and Biological Engineering (Springer, 2009), pp. 1536–1539

[49]J.S. Cunha, M. Coimbra, P. Campos, J.M. Soares, Medical Imaging, IEEE Transactions on 27(1), 19 (2008)

[50]J. Lee, J. Oh, S.K. Shah, X. Yuan, S.J. Tang, in Proceedings of the 2007 ACM symposium on Applied computing (ACM, 2007), pp. 1041–1045

[51]F. Vilarino, P. Spyridonos, O. Pujol, J. Vitria, P. Radeva, F. De Iorio, in Pattern Recognition, 2006. ICPR 2006. 18th International Conference on, vol. 4 (IEEE, 2006), vol. 4, pp. 719–722

[52]F. Vilarino, P. Spyridonos, F. DeIorio, J. Vitria, F. Azpiroz, P. Radeva, Medical Imaging, IEEE Transactions on 29(2), 246 (2010)

[53]X. Zabulis, A.A. Argyros, D.P. Tsakiris, in Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on (IEEE, 2008), pp. 3921–3926

[54]F. Vilari˜no, L.I. Kuncheva, P. Radeva, Pattern Recognition Letters 27(8), 875 (2006)

[55]S.A. Karkanis, D.K. Iakovidis, D.E. Maroulis, D.A. Karras, M. Tzivras, Information Technology in Biomedicine, IEEE Transactions on 7(3), 141 (2003)

[56]M. H¨afner, M. Liedlgruber, A. Uhl, A. V´ecsei, F. Wrba, Medical image analysis 16(1), 75 (2012)

[57]M. Drozdzal, L. Igual, J. Vitria, C. Malagelada, F. Azpiroz, P. Radeva, in Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on (IEEE, 2010), pp. 117–124

[58]A Novel Algorithm for Color Similarity Measurement and the Application for Bleeding Detection in WCE

[59]Choudhary, Dolly, et al. "Performance analysis of texture image classification using wavelet feature." International Journal of Image, Graphics and Signal Processing 5.1 (2013): 58.

[60]Hai, Tran Son, and Nguyen Thanh Thuy. "Image classification using support vector machine and artificial neural network." International Journal of Information Technology and Computer Science (IJITCS) 4.5 (2012): 32.