Segmentation of Crohn, Lymphangiectasia, Xanthoma, Lymphoid Hyperplasia and Stenosis diseases in WCE

Wireless capsule endoscopy (WCE) is a great breakthrough for Gastrointestinal (GI) Tract diagnoses which can view the entire gastrointestinal tract, especially the small bowel, without invasiveness and sedation. However, a tough problem associated with this new device is that too many images to be inspected by naked eyes is difficult for physicians, Thus it is essential to find an automatic and intelligent diagnosis method to help physicians. In this paper, a new segmentation algorithm for detection of Lymphangiectasia, Xanthoma, Crohn, and Stenosis in WCE images is proposed. This new approach mainly uses the HSV color space, sigmoid function and canny edge detector. We compare our method with a fuzzy c-mean clustering. We show that sensitivities of the sigmoid function for Lymphangiectasia, Lymphoid hyperplasia, severe Crohn's disease, Xanthoma and ulcerated Stenosis are respectively 89.32%, 91.27%, 95.45%, 87.01%, 97% and sensitivities of the fuzzy c-means clustering with same order are 83.91%, 86.7%, 96.38%, 90.4%, 93.83%. Totally, the sigmoid function is more specific and sensitive, with same accuracy.