An objective evaluation method of ulcerative colitis with optical colonoscopy images based on higher order local auto-correlation features

This study aims to establish a new method of objective evaluation for optical colonoscopy that can quantify the severity of colonic mucosa for ulcerative colitis (UC). UC is an intractable disease and has been the subject of survey research for long time. However, because there are enormous variations in the patterns of symptoms associated with UC, universal diagnostic standards have yet to be established. Accordingly, diagnostic accuracy is highly dependent on the experience and knowledge of the medical doctor. In order to overcome this problem, this paper describes a method of objective evaluations for UC based on image recognition techniques and multi-discriminant analysis. The proposed method extracts geometrical features using higher order local auto-correlations from the saturation element of the HSV color space for the colonoscopy images, and makes classifications according to the UC severity based on the subspace method. This study provides an index for UC severity to support colonoscopy diagnosis.