Analysis of wireless capsule endoscopy images using chromaticity moments

The wireless capsule endoscopy (WCE) invented by given imaging has gradually been widely used in hospitals due to its great breakthrough that it can view the entire small bowel with no sedation and almost no invasiveness compared to traditional endoscopies and other imaging techniques 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 very significant to help the physician do analysis or diagnosis using computerized methods. In this paper, a new method aimed for abnormal region detection in WCE images is proposed. This new approach mainly focuses on local color feature, also a very powerful clue for the physicians to diagnose, to judge the status of the gastrointestinal tract. We propose a new idea of chromaticity moments as the features to discriminate normal regions and abnormal regions, which make full use of the Tchebichef polynomials and the illumination invariant of the HSI color space. Experimental results on our present image data sets show that it is feasible to exploit the chromaticity features to analyse the wireless capsule endoscopy images.