WCE Abnormality Detection Based on Saliency and Adaptive Locality-Constrained Linear Coding

Wireless capsule endoscopy (WCE) has become a widely used diagnostic technique for the digestive tract, at the price of a large volume of data that needs to be analyzed. To tackle this problem, a new computer-aided system using novel features is proposed in this paper to classify WCE images automatically. In the feature learning stage, to obtain the representative visual words, we first calculate the color scale invariant feature transform from the bleeding, polyp, ulcer, and normal WCE image samples separately and then apply K-means clustering on these features to obtain visual words. These four types of visual words are combined together to composite the representative visual words for classifying the WCE images. In the feature coding stage, we propose a novel saliency and adaptive locality-constrained linear coding (SALLC) algorithm to encode the images. The SALLC encodes patch features based on adaptive coding bases, which are calculated by the distance differences among the features and the visual words. Moreover, it imposes the patch saliency constraint on the feature coding process to emphasize the important information in the images. The experimental results exhibit a promising overall recognition accuracy of 88.61%, validating the effectiveness of the proposed method. Note to Practitioners-Because of approximately 50 000 wireless capsule endoscopy (WCE) images for one patient, a clinician usually has to spend about 2 h to view these images and make a diagnostic decision on possible gastrointestinal diseases. Therefore, it is crucial to design an automatic computer-aided system to assist clinicians to classify images with abnormal structures. However, most WCE abnormality detection methods consider only one specific abnormality and the existing multiabnormality classification results are far from satisfactory. Thus, we propose a novel automatic multiabnormality WCE image detection scheme, namely, saliency and adaptive locality-constrained linear coding algorithm, by considering the local coding bases adaptively and the saliency information about the images. Results from comprehensive comparison experiments suggest that the proposed computer-aided classification system achieves improved accuracy.