A feature extraction scheme from region of interest of wireless capsule endoscopy images for automatic bleeding detection

Wireless capsule endoscopy (WCE) is an effective but painless video technology to detect small intestine diseases like bleeding. For analyzing WCE video frames, instead of using the most common RGB (red, green, blue) color scheme, in this paper, CMYK (Cyan-C, Magenta-M, Yellow-I and Black-K) color scheme is used, which is subtractive color model and more effective for color separation. First, a region of interest (ROI) is determined using YIQ (luminance-Y, chrominance-IQ: in phase-I and quadrature-Q) color scheme depending on the Q value of the pixels and some morphological operations. Next, CMYK values are calculated within the ROI pixels. Instead of considering single color space all color spaces are investigated to extract feature, among them four statistical measures as mean of four color space is proposed. It is shown that use of ROI and CMYK color space not only reduces computational complexity but also offers significantly better discrimination between bleeding and non-bleeding pixels. For the purpose of classification, support vector machine (SVM) classifier is employed. From extensive experimentation on several WCE videos collected from a publicly available database, it is observed that the bleeding detection performance of the proposed method in terms of accuracy, sensitivity and specificity is quite satisfactory in comparison to that obtained by some of the existing methods.