Bag-Of-Visual-Words Approach based on SURF Features to Polyp Detection in Wireless Capsule Endoscopy Videos

Wireless Capsule Endoscopy (WCE) is a relatively new technology (FDA approved in 2002) allowing doctors to view most of the small intestine. One of the most important goals of WCE is the early detection of colorectal polyps. We introduce “Bag-of-Visual-Words” method which has been successfully used in particular for image classification in non-medical domains. Initially the training image patches are sampled and represented by speeded up robust features (SURF) descriptor, and then the bag of words model is constructed by K-means clustering algorithm. Subsequently the document is represented as the histogram of the visual words which is the feature vector of the image. Finally, a SVM classifier is trained using these feature vectors to distinguish images with polyp regions from ones without them. Our preliminary experiments on our current data set demonstrate that the proposed method achieves promising performances.