Ulcer Detection in Wireless Capsule Endoscopy Using Locally Computed Features

WCE (Wireless Capsule Endoscopy) has become one of the most significant inventions for detecting different types of digestive tract diseases of humans. Distinct types of abnormalities like polyps, ulcer, tumor and intestine cancer are diagnosed by the clinicians with the implement of WCE in a convenient way. In order to deduce the incubus of the physicians an automated and efficient recognition system is required. In this paper, an advanced method for automatically detecting ulcers in the images of the WCE video record is proposed using the HSV color model. Region of interest (ROI) was identified applying a threshold on images that were extracted from the video of WCE. Local features have been extracted only from the ROI which is usually a small part of an image that offers a low computational cost. Linear discriminant analysis has been used for the separation of ulcer and non-ulcer images. The proposed algorithm was tested on a publicly available database. The performance has obtained accuracy 87.55%, sensitivity 94.70%, specificity 83.30%, precision 75.00% and F1 score 83.70%. Hence, the proposed method outperforms an efficient method that will create a great impact in this research arena.