An asymmetric indexed image based technique for automatic ulcer detection in wireless capsule endoscopy images

This paper proposes an automatic technique to detect ulcer frames from wireless capsule endoscopy (WCE) videos utilizing the histogram of asymmetric RGB indexed image. Incorporating asymmetry in calculating the indexed image allows to impose higher priority to more informative color plane and lower priority to less informative color plane. Therefore, in this paper, a color histogram extracted from asymmetric indexed image is proposed as feature, instead of conventional histogram from original WCE image. Exhaustive experimentation on publicly available WCE video database validate that significant differences can be obtained between ulcer and non-ulcer images in histogram patterns of asymmetric indexed image. The supervised support vector machine (SVM) classifier with Gaussian radial basis function (RBF) kernel is used to evaluate the classification performance.