Mucosal Region Detection in Capsule Endoscopy videos

Abstract:

Wireless capsule endoscopy (WCE) provides an inner view of the human intestinal system, and helps physicians to identify abnormalities. Usually the WCE produces a huge amount of data (which, in terms of video recordings and per patient, is about 8 hours and approximately 55000 frames of RGB color data). Digital image processing techniques can be used effectively to reduce this burden, by making use of automatic algorithms. The inner tubular like structure of the human large intestinal tract consist of two major regions. One is the lumen, the intermediate region where the capsule moves. The other part is the mucosa, which is a mucus-secreting membrane lining the lumen cavities. Abnormal tissues and lesions, like ulcers and polyps, are usually seen as exterior parts of the mucosa, and the lumen is filled with intestinal juices of the human digestive system. Here we apply variational models, active contours [1, 2, 3] and adaptive thresholding [4] for segmenting mucosa regions in WCE images and videos. These allow for fast implementations to segment WCE videos in real time. Results on WCE images and videos show that the approach provides accurate segmentations in an efficient manner.

Examples of the proposed segmentation methodology:

The following examples illustrates efficient mucosa segmentation using our scheme [1] for different wireless capsule endoscopy frames.

Wireless capsule endoscopy images with segmentation curve (in white color) from our active contour based scheme [1]

References:

[1]* V. B. S. Prasath, I. N. Figueiredo, P. N. Figueiredo. Colonic mucosa detection in wireless capsule endoscopic images and videos. Congress on Numerical Methods in Engineering (CMNE 2011), Coimbra, Portugal, Jun 14-17, 2011.

[2] V. B. S. Prasath, I. N. Figueiredo, P. N. Figueiredo, K. Palaniappan. Mucosal region detection and 3D reconstruction in wireless capsule endoscopic videos by active Contours. 34th Annual International Conference IEEE EMBS (EMBC), San Diego, CA, 2012. Proc. IEEE, pp. 4014-4017.

[3] V. B. S. Prasath, R. Delhibabu. Automatic image segmentation for video capsule endoscopy. International Conference on Computation Intelligence: Health and Disease (CIHD), Visakhapatnam, India, December 2014. Proc. Computational Intelligence in Medical Informatics, (eds. N. B. Muppalaneni, V. K. Gunjan), pp. 73-80, 2015. Springer Briefs in Forensic and Medical Bioinformatics.

[4] V. B. S. Prasath, R. Delhibabu. Automatic mucosa detection in video capsule endoscopy with adaptive thresholding. International Conference on Computation Intelligence and Data Mining (ICCIDM), Bhubaneswar, India. Proc. Computational Intelligence in Data Mining, (eds. H. S. Behera, D. P. Mohapatra), Springer AISC 410, pp. 95-102, Dec 2015.

 Acknowledgement:

*The work [1] was done while the first author was at the Department of Mathematics, University of Coimbra, PT. The first author also gratefully acknowledges Prof. Richard Tsai (UTAustin) for his advise and help.

*The work [1] was partially supported by the research project UTAustin/MAT/0009/2008 of the UT Austin|Portugal Program (http://www.utaustinportugal.org/) and by CMUC and FCT (Portugal), through European program COMPETE/FEDER.

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