City Research Online

Abstract

The ileocecal valve (ICV) is a common source of false-positive (FP) detections in CT colonography (CTC) computer-aided detection (CAD) of polyps. In this paper, we propose an automatic method to identify ICV CAD regions to reduce FPs. The ICV is a particularly challenging structure to detect due to its variable, polyp-mimicking morphology. However, the vast majority of ICVs have a visible orifice, which appears as a 3D concave region. Our method identifies the orifice concave region using a partial differential equation (PDE) based on 3D curvature and geometric constraints. These orifice features, combined with intensity and shape features generated in a Bayesian framework, comprise a set of compact features fed into an Adaboost classifier to produce a final classification of a region being ICV or non-ICV. Experimental results on a multi-center tagged CTC dataset demonstrate the success of the method in detecting ICV regions and reducing FPs in CAD.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords:	colon CAD, Principle curvature flow, Bayesian methods, Ileocecal valve (ICV) detection
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine
Departments:	School of Science & Technology > Computer Science

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