Title :
A Stochastic Polygons Model for Glandular Structures in Colon Histology Images
Author :
Sirinukunwattana, Korsuk ; Snead, David R. J. ; Rajpoot, Nasir M.
Author_Institution :
Dept. of Comput. Sci., Univ. of Warwick, Coventry, UK
Abstract :
In this paper, we present a stochastic model for glandular structures in histology images of tissue slides stained with Hematoxylin and Eosin, 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 a likelihood for the presence of a glandular structure. The inference is made via a Reversible-Jump Markov chain Monte Carlo simulation. To the best of our knowledge, all existing published algorithms for gland segmentation are designed to mainly work on healthy samples, adenomas, and low grade adenocarcinomas. One of them has been demonstrated to work on intermediate grade adenocarcinomas at its best. Our experimental results show that the RPM yields favorable results, both quantitatively and qualitatively, for extraction of glandular structures in histology images of normal human colon tissues as well as benign and cancerous tissues, excluding undifferentiated carcinomas.
Keywords :
Markov processes; Monte Carlo methods; biological tissues; cancer; cellular biophysics; image segmentation; medical image processing; organic compounds; Bayesian inference problem; RPM; benign; cancerous tissues; colon histology imaging; eosin; epithelial nuclei; gland segmentation; glandular structures; hematoxylin; human colon tissues; intermediate grade adenocarcinomas; low grade adenocarcinomas; random number; random polygons model; reversible-Jump Markov chain Monte Carlo simulation; stochastic polygons model; tissue slides; undifferentiated carcinomas; Bayes methods; Colon; Glands; Image color analysis; Image segmentation; Markov processes; Silicon; Bayesian inference; gland modeling; histology image analysis; random polygons; reversible-jump Markov chain Monte Carlo;
Journal_Title :
Medical Imaging, IEEE Transactions on
DOI :
10.1109/TMI.2015.2433900