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