A random polygons model of glandular structures in colon histology images
In this paper, we present a stochastic model for glandular structures in Hematoxylin and Eosin stained histology images, choosing colon tissue as an example. The proposed Random Polygons Model (RPM) treats each glandular structure in an image as a polygon made of a random number of vertices, where the vertices represent approximate locations of epithelial nuclei. We formulate the RPM as a Bayesian inference problem by defining a prior for spatial connectivity and arrangement of neighboring epithelial nuclei and likelihood about the presence of glandular structure. The inference is made via a Reversible-Jump Markov Chain Monte Carlo simulation. Our experimental results show that the RPM yields favorable results, both quantitatively and qualitatively, for extraction of glandular regions in histology images of human colon tissue.