An automatic ulcer detection scheme using histogram in YIQ domain from wireless capsule endoscopy images

Being one of the most effective video technologies, wireless capsule endoscopy (WCE) offers the physicians to diagnose the gastrointestinal (GI) diseases like ulcer non-invasively. Physicians, while analyzing the WCE videos, find it tedious to detect ulcer because of the huge amount of image frames present in WCE videos. This tedious reviewing process at times leads to inaccuracy in diagnosing ulcer. This paper proposes an automatic technique to detect ulcer frames from WCE videos utilizing the histogram in Y plane of Y I Q color space which utilizes human color-response characteristics. Exhaustive experimentation on publicly available WCE video database validate that significant differences can be obtained between ulcer and non-ulcer images in histogram patterns of Y plane. Cumulative pixel number in Y plane over an optimum threshold is chosen as feature through histogram analysis. Moreover, advantage in computation and implementation is ensured through the proposed 1-D feature for ulcer detection. The supervised support vector machine (SVM) classifier with Gaussian radial basis function (RBF) kernel is used to evaluate the classification performance.

[1]  Yaoqin Xie,et al.  A new approach to detecting ulcer and bleeding in Wireless capsule endoscopy images , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[2]  S. H. Dabhole,et al.  An optimal IMF selection based on fast BEEMD with Dlac analysis for detection of Polyp and Ulcer in WCE images , 2015, 2015 2nd International Conference on Electronics and Communication Systems (ICECS).

[3]  Varun P. Gopi,et al.  A new method for ulcer detection in endoscopic images , 2015, 2015 2nd International Conference on Electronics and Communication Systems (ICECS).

[4]  Max Q.-H. Meng,et al.  Computer-Aided Detection of Bleeding Regions for Capsule Endoscopy Images , 2009, IEEE Transactions on Biomedical Engineering.

[5]  H Schwacha,et al.  [Capsule endoscopy]. , 2005, Praxis.

[6]  G. Iddan,et al.  Wireless capsule endoscopy , 2003, Gut.

[7]  Chengjun Liu,et al.  New image descriptors based on color, texture, shape, and wavelets for object and scene image classification , 2013, Neurocomputing.

[8]  João Barroso,et al.  Intrinsic higher-order correlation and lacunarity analysis for WCE-based ulcer classification , 2012, 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS).

[9]  Nikolaos G. Bourbakis,et al.  Identification of ulcers in Wireless Capsule Endoscopy videos , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[10]  Tonmoy Ghosh,et al.  Automatic Small Intestinal Ulcer Detection in Capsule Endoscopy Images , 2016 .

[11]  D. Altman,et al.  Statistics Notes: Diagnostic tests 1: sensitivity and specificity , 1994 .

[12]  P. Swain,et al.  Wireless capsule endoscopy. , 2002, The Israel Medical Association journal : IMAJ.

[13]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[14]  Max Q.-H. Meng,et al.  Using ensemble classifier for small bowel ulcer detection in wireless capsule endoscopy images , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).