Ulcer detection and classification of wireless capsule endoscopy images using RGB masking

Wireless Capsule Endoscopy (WCE) is becoming an extensively accepted method used by physicians to diagnose diseases which affect small intestine. During the WCE examination of patients, a series of images are acquired to generate a video footage. Inspecting a WCE video is laborious and time-consuming that results in a limited application of WCE. Therefore, it would be extremely advantageous to develop an intelligent algorithm to inspect the WCE images. This research aims to develop an algorithm to enhance wireless capsule endoscopy images and analyses them to detect Ulcer located in small intestine. This algorithm of capsule images can be clinically very useful as it can discriminate between normal and abnormal frame. It also provides correlative information for huge set of data. We have proposed color detection method in RGB color space as it is a key technique of color image interpretation and recognition, as well as suitable for Ulcer detection in WCE images. Support Vector Machine (SVM) used to classify frames according to findings. Analysis is done on basis of pixel and frame information to check whether it is detected correctly or not. © 2016 American Scientific Publishers. All rights reserved.