Online redundant image elimination and its application to wireless capsule endoscopy
Digestive tract examination has now become painless and simple with the aid of wireless capsule endoscopy (WCE). By continuously imaging and transmitting the patients gastrointestinal tract, the capsule can store thousands of endoscopic images for medical diagnosis. The problem is that a large number of images generated in the near-time period are near-reduplicate and are therefore time-consuming for a medical expert to inspect. In this paper, we tackle this problem by proposing an online redundant image elimination system. Computer visionbased primitive features are first explored and compared to represent WCE images. A spatial sensitive version is then introduced to enhance the description. After determining the dissimilarity between neighboring images with a suitable measure, real-time and image-buffered versions of peak detectionbased elimination algorithms are proposed to retain the distinctive/interesting images in the examination. Comparison results of primitive features and their spatial sensitive approaches via several probabilistic dissimilarity measures demonstrate that they are descriptive and efficient. Also, experiments performed on both the testing and the large real datasets show that the proposed elimination algorithms are effective, flexible and robust.
wireless capsule endoscopy image features redundancy elimination control chart