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.
Keywords: RGB Color Space; Support Vector Machines (SVMs); Ulcer in Small Bowel (Small Intestine); Wireless Capsule Endoscopy (WCE)
Document Type: Research Article
Affiliations: Center of Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS, 32610 Tronoh, Perak, Malaysia
Publication date: 01 October 2016
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