Small bowel tumor detection for wireless capsule endoscopy images using textural features and support vector machine

Abstract

Wireless capsule endoscopy (WCE) has been gradually applied in hospitals due to its great advantage that it can directly view the entire small bowel in human body compared with traditional endoscopies and other imaging techniques for gastrointestinal diseases. However, a challenging problem with this new technology is that too many images produced by WCE causes a tough task to doctors, so it is very significant to help and relief the clinicians if we can develop computer based automatic detection system to prescreen the collected large amount of images and identify the images with potential problems. In this paper, we propose a new scheme aimed for small bowel tumor detection of WCE images. This new scheme utilizes texture feature, also a powerful clue used by physicians, to detect tumor images with support vector machine. We put forward a new idea of wavelet based local binary pattern as the textural features to discriminate tumor regions from normal regions, which take advantage of wavelet transform and uniform local binary pattern. With support vector machine as the classifier, three-fold cross validation experiments on our present image data verify that it is promising to employ the proposed texture features to recognize the small bowel tumor regions.