Wireless capsule endoscopy video automatic segmentation

Abstract

Wireless capsule endoscopy (WCE) is an advanced technology that allows diagnosis inside human's digestive tract without invasiveness, however, it is a time-consuming task for clinicians to diagnose due to the large number of frames in video. A novel and efficient algorithm is proposed in this paper to help clinicians segment the WCE video automatically according to stomach, small intestine, and large intestine regions. Firstly, since there are many impurities and bubbles in WCE video frames which add the difficulty of segmentation, a pre-procedure is presented to denote the valid regions in the frames based on color and wavelet texture features. Secondly, the boundaries between adjacent organs of WCE video are estimated in two levels which consist of a rough and a fine level. In the rough level, color feature is utilized to draw a dissimilarity curve between frames and the aim is to find the peak of the curve, which represents the approximate boundary we want to locate. In the fine level, Hue-Saturation histogram color feature in HSI color space and uniform LBP texture feature from grayscale images are extracted. And support vector machine (SVM) classifier is utilized to segment the WCE video into different regions. The experiments demonstrate a promising efficiency of the proposed algorithm and the average precision and recall achieve as high as 94.33% and 89.50% respectively.