Concavity analysis for reduction of ileocecal valve false positives in CTC

Ye, Xujiong and Slabaugh, Greg (2011) Concavity analysis for reduction of ileocecal valve false positives in CTC. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA.

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Concavity analysis for reduction of ileocecal valve false positives in CTC

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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.

Additional Information: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.
Keywords:Colon CAD, Principle curvature flow, Bayesian methods, Ileocecal valve (ICV) detection
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:7315
Deposited On:23 Jan 2013 15:04

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