A locally based feature descriptor for abnormalities detection

Abstract

Wireless capsule endoscopy (WCE) is a novel imaging technique that can view the entire small bowel in human body. Therefore, it has been gradually adopted compared with traditional endoscopies for gastrointestinal diseases. However, the task of reviewing the vast amount of images produced by a WCE test is exhaustive for the physicians. This paper presents a new feature extraction scheme for pathological inflammation and ulcer regions discrimination in WCE images. In addition, the novel approach is adopted for polyp recognition in colonoscopy videos. A novel idea based on extracting certain local features from the image is proposed. Then, the occurrence histogram of these features is used as descriptor of the image. The new feature descriptor scheme is grayscale rotation invariant and computationally simple as the operator can be realized with a few operations in a small neighborhood. The proposed operator does not discard the contrast information. Besides, we propose to test the quality of the model using logarithmic loss metric and show how calibration can be useful in reducing the aforementioned measure. Extensive classification experiments have been applied on different datasets, which prove that the occurrence histogram of the extracted features is powerful. The proposed method achieved 99.1%, 99.7% and 99.2% in terms of the precision in the first, second and third datasets, respectively, and surpassed some known local descriptors on a texture dataset.