Informative and Uninformative Regions Detection in WCE Frames

Abstract

Wireless capsule endoscopy (WCE) is relatively a new device which investigates the entire gastrointestinal (GI). About 55000 frames are captured during an examination (two frames per second). Thus, it is beneϐicial to ϐind an automatic method to detect diseases frames or regions of a frame. The WCE videos have lots of uninformative regions (such as intestinal juice, bubbled, and dark regions); therefore, preprocessing is useful and necessary in diseases detection. In this paper, three practical methods are introduced to detect the informative and uninformative regions in a frame. In order to achieve this goal, morphological operations, fuzzy k-means, sigmoid function, statistic features, Gabor ϐilters, Fisher test, neural network, and discriminators in the HSV color space are used to detect uninformative regions (do not carry clinical information) in a frame. Our experimental results indicate that precision, sensitivity, accuracy, and speciϐicity are respectively 97.76%, 97.80%, 98.15%, and 98.40% in the ϐirst method, 93.32%, 84.60%, 91.05%, and 95.67%, respectively in the second one, and 93.32%, 84.60%, 91.05%, and 95.67%, respectively in the third method.