Wireless capsule endoscopy image classification based on vector sparse coding

Wireless capsule endoscopy (WCE) is a promising technology for gastrointestinal disease detection. Since there are more than 50,000 frames in one WCE video of a patient, classifying the whole frame set of the digestive tract into subsets corresponding to esophagus, stomach, small intestine, and colon is necessary, which can help physicians review and diagnose rapidly and accurately. The digestive organ classification in WCE is a challenging task due to the difficulties in feature representation of WCE images. This paper presents a new method of WCE organ classification by incorporating a proposed locality constraint based vector sparse coding (LCVSC) algorithm with the support vector machine classifier. Experimental results validate the effectiveness of the proposed method and it is encouraging to see that a good classification performance is achieved.