Classification of colorectal polyp regions in optical projection tomography

Abstract

The potential of optical projection tomography (OPT) to enhance colorectal polyp diagnosis is beginning to be explored. This paper presents, to the best of our knowledge, the first study on automatic image analysis of OPT images of colorectal polyps. 3D regions are classified using the bag of visual words framework and support vector machines. Independent subspace analysis is used to learn a domain-specific feature dictionary. This is compared to the use of raw patches (after random projection) and local binary patterns. Classification experiments (across patients) at the patch level and at the region level are presented using a set of 30 expert-annotated OPT images. Results show that accurate classification of 3D OPT image regions is feasible using this approach; regions of low-grade dysplasia and invasive cancer were discriminated with approximately 90% accuracy.