Detection of small colon bleeding in wireless capsule endoscopy videos.

In the recent years, wireless capsule endoscopy (WCE) technology has played a very important role in diagnosing diseases within the gastro intestinal (GI) tract of human beings. The WCE device captures images of the GI tract of patient with a certain frame rate. Physicians examine these images in order to find abnormalities in the GI tract. This examination process is very time consuming and hectic for the physician as a WCE device captures around 60,000 images on the average. At present, there are no standards defined for the WCE image classification. Computer aided methods help reducing the burden on the physicians by automatically detecting the abnormalities in the GI tract such as small colon bleeding. In this paper, a pixel based approach to detect bleeding regions in the WCE videos by using a support vector classifier is proposed. Threshold analysis in HSV color space is performed to compute the features for training an optimal support vector machine. The HSV features of the WCE images are fed to the trained support vector classifier for classification. Also, our method includes image enhancement and edge removal in WCE images, which is done prior to classification, for robust results. The method offers high sensitivity, specificity and accuracy in terms of correctly classifying images that contain bleeding regions as compared to another contemporary method. A detailed experimental analysis is also provided for the purpose of method evaluation.