Polyp classification by Weber's Law as texture descriptor for clinical colonoscopy.

Weber's law for image feature descriptor (WLD) is based on the theory that the ratio of the increment threshold to the background intensity is a constant. It has been used in facial recognition, structure detection, and tissue classification in X-ray images. In this paper, WLD is explored in the polyp classification in color colonoscopy images for the first time. An open, on-line colonoscopy image database is used to evaluate the new descriptor. The database contains 74 polyps, including 19 benign polyps and 55 malignant ones. Each polyp has a white light image (WLI) and a narrow band image (NBI), both were obtained by the same fibro-colonoscopy from the same patient. WLD image texture features are extracted from three color channels of (1) color WLI, (2) color NBI and (3) WLI+NBI. The extracted features are analyzed, ranked and classified using a Random Forest package based on the merit of the area under the curve (AUC) of the Receiver Operating Characteristics (ROC). The performance of WLD is quantitatively documented by the AUC, the ROC curve, the P-R (precision-recall) plot and the accuracy measure with comparison to commonly used features, such as Haralick and local binary pattern feature descriptors. The results demonstrate the advantage of WLD in the polyp classification in terms of the quantitative measures.