A novel method for capsule endoscopy video automatic segmentation

Wireless capsule endoscopy (WCE) is a recently developed revolutionary medical technology which records the video of human's digestive tract noninvasively. However, reviewing a WCE video is a tired and time-consuming task for clinicians. Thus, WCE video automatic segmentation methods are emerging to reduce the review time for clinicians. In our previous work, a two-level WCE video segmentation approach has been proposed, which provides a novel approach to localize the boundaries more exactly and efficiently. However, it has an unsatisfactory performance in the small intestine/large intestine boundary detection. In this paper, we propose new features and an improved classifier to improve the previous two-level segmentation algorithm. In the rough level, color feature is utilized to draw a dissimilarity curve and an approximate boundary has been obtained. At the same time, training data for fine level can be directly labeled and collected between the two approximate boundaries of organs to overcome the difficulty of training data acquisition. In the fine level, a novel color uniform local binary pattern (CULBP) algorithm is proposed, which includes two kinds of patterns, color norm patterns and color angle patterns. The CULBP feature is more robust to variation of illumination and more discriminative for classification. Moreover, in order to elevate the performance of SVM classifier we proposed the Ada-SVM classifier which using RBFSVMs as component of Adaboost classifier. At last, an analysis of classification results of the Ada-SVM classifier is carried out to segment the WCE video into several meaningful parts, stomach, small intestine and large intestine. The experiments demonstrate a promising performance of the proposed method. The average precision and recall are as high as 91.37% and 88.50% in stomach/small intestine classification, 90.35% and 97.28% in small intestine/ large intestine classification.