Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments.

The wireless capsule endoscopy (WCE) invented by Given Imaging has been gradually used in hospitals due to its great breakthrough that it can view the entire small bowel for gastrointestinal diseases. However, a tough problem associated with this new technology is that too many images to be examined by eyes cause a huge burden to physicians, so it is significant if we can help physicians do diagnosis using computerized methods. In this paper, a new method aimed for bleeding and ulcer detection in WCE images is proposed. This new approach mainly focuses on color feature, also a very powerful clue used by physicians for diagnosis, to judge the status of gastrointestinal tract. We propose a new idea of chromaticity moment as the features to discriminate normal regions and abnormal regions, which make full use of the Tchebichef polynomials and the illumination invariant of HSI color space, and we verify performances of the proposed features by employing neural network classifier. Experimental results on our present image data of bleeding and ulcer show that it is feasible to exploit the proposed chromaticity moments to detect bleeding and ulcer for WCE images.