Bleeding Detection in Wireless Capsule Endoscopy Image Video Using Superpixel-Color Histogram and a Subspace KNN Classifier

Wireless Capsule Endoscopy (WCE) has become increasingly popular in clinical gastrointestinal (GI) disease diagnosis, benefiting from its painless and noninvasive examination. However, reviewing a large number of images is time-consuming for doctors, thus a computer-aided diagnosis (CAD) system is in high demand. In this paper, we present an automatic bleeding detection algorithm that consists of three stages. The first stage is the preprocessing, including key frame extraction and edge removal. In the second stage, we discriminate the bleeding frames using a novel superpixel-color histogram (SPCH) feature based on the principle color spectrum, and then the decision is made by a subspace KNN classifier. Thirdly, we further segment the bleeding regions by extracting a 9-D color feature vector from the multiple color spaces at the superpixel level. Experimental results with an accuracy of 0.9922 illustrate that our proposed method outperforms the state-of-the-art methods in GI bleeding detection with low computational costs.