Ulcer detection in wireless capsule endoscopy images

The invention of wireless capsule endoscopy greatly helps physician to view small intestine images without causing much pain to patients. It becomes very popular around the world for its usability and performance. However, physician requires a long time (around 45 minutes) to examine a capsule endoscopy video generated from each examination. In this paper, we propose a new image processing method using combination of local features for ulcer detection. The proposed method is developed based on bag-of-words model and feature fusion technique. Image patches are described by LBP and SIFT features. The pyramid bag-of-words is employed to model and represent the images, and SVM classifiers are trained. Finally feature fusion technique is employed to draw a final conclusion. Experimental results show that the proposed method outperforms single feature methods and existing methods.