A comparative study of shape features for polyp detection in wireless capsule endoscopy images

Wireless capsule endoscopy (WCE) has been gradually employed in hospitals because it can directly view the entire small bowel of a human body for the first time. However, a troublesome problem related to this new technology is that too many images produced by WCE will take a lot of efforts for doctors to inspect. In this paper, we propose a comparative study of shape features aiming for intestinal polyp detection for WCE images. As polyps exhibit strong shape characteristics, also a powerful clue used by physicians, we investigate two kinds of shape features, MEPG-7 region-based shape descriptor and Zernike moments, in our study. With multi-layer perceptron neural network as the classifier, experiments on our present image data show that it is promising to employ both Zernike moments and MEPG-7 region-based shape descriptor as the shape features to recognize the intestinal polyp regions, and a better performance is obtained by the Zernike moments based shape features.