Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images

Abstract

Wireless capsule endoscopy (WCE) is a novel imaging technique that can travel through human body and image the small bowel entirely. Therefore, it has been gradually adopted compared with traditional endoscopies for gastrointestinal diseases. However, the big number of the produced images by a WCE test makes their review exhaustive for the physicians. It is helpful for clinicians if we can develop a computer-aided diagnosis system for the task of identifying the images with potential problems. The aim of this paper is to automatize the process of WCE images abnormalities detection by presenting a new texture extraction scheme for pathological inflammation, polyp, and bleeding regions discrimination in WCE images. A new approach based on local binary pattern variance and discrete wavelet transform is proposed. The new textural features scheme has many advantages, e.g., it detects multi-directional characteristics and overcomes the illuminations changes in WCE images. Intensive experiments are conducted on two datasets constructed from several WCE exams. The promising results make the presented method suitable for abnormalities detection in WCE images.