Bleeding detection in wireless capsule endoscopy based on color features from histogram probability

This paper presents a novel technique for detecting bleeding regions in capsule endoscopy images. The proposed algorithm extracts color features from image-regions by calculating mean, standard deviation, skew and energy from the first order histogram of the RGB planes separately. Through the use of RGB color space, three times more number of features can be obtained than while using a grayscale image. Such color features have been used in content based retrieval system in pathology images. However, in spite of simplicity and ease of calculation, these features have not yet been studied in the classification of bleeding and non-bleeding regions in capsule endoscopic images. This paper studies the feasibility of using these features by assessing all possible feature subsets through the use of classification accuracy. The proposed algorithm could obtain classification accuracy up to 89%.