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Wireless Capsule Endoscopy (WCE) is relatively a new technology to examinethe entire GI trace. During an examination, it captures more than 55,000frames. Reviewing all these images is time-consuming and prone to human error.It has been a challenge to develop intelligent methods assisting physicians toreview the frames. The WCE frames are captured in 8-bit color depths whichprovides enough a color range to detect abnormalities. Here, superpixel basedmethods are proposed to segment five diseases including: bleeding, Crohn'sdisease, Lymphangiectasia, Xanthoma, and Lymphoid hyperplasia. Two superpixelsmethods are compared to provide semantic segmentation of these prolificdiseases: simple linear iterative clustering (SLIC) and quick shift (QS). Thesegmented superpixels were classified into two classes (normal and abnormal) bysupport vector machine (SVM) using texture and color features. For bothsuperpixel methods, the accuracy, specificity, sensitivity, and precision(SLIC, QS) were around 92%, 93%, 93%, and 88%, respectively. However, SLIC wasdramatically faster than QS.

Superpixels Based Segmentation and SVM Based Classification Method to Distinguish Five Diseases from Normal Regions in Wireless Capsule Endoscopy