A novel texture descriptor for detection of glandular structures in colon histology images

The first step prior to most analyses on most histopathology images is the detection of area of interest. In this work, we present a superpixel-based approach for glandular structure detection in colon histology images. An image is first segmented into superpixels with the constraint on the presence of glandular boundaries. Texture and color information is then extracted from each superpixel to calculate the probability of that superpixel belonging to glandular regions, resulting in a glandular probability map. In addition, we present a novel texture descriptor derived from a region covariance matrix of scattering coefficients. Our approach shows encouraging results for the detection of glandular structures in colon tissue samples.