Delaunay triangulation-based pit density estimation for the classification of polyps in high-magnification chromo-colonoscopy

In this work we propose a method to extract shape-based features from endoscopic images for an automated classification of colonic polyps. This method is based on the density of pits as used in the pit pattern classification scheme which is commonly used for the classification of colonic polyps. For the detection of pits we employ a noise-robust variant of the LBP operator. To be able to be robust against local texture variations we extend this operator by an adaptive thresholding. Based on the detected pit candidates we compute a Delaunay triangulation and use the edge lengths of the resulting triangles to construct histograms. These are then used in conjunction with the k-NN classifier to classify images. We show that, compared to a previously developed method, we are not only able to almost always get higher classification results in our application scenario, but that the proposed method is also able to significantly outperform the previously developed method in terms of the computational demand.