City Research Online

Abstract

This paper presents an automated detection method to identify colonic polyps and reduce false positives (FPs) in CT images. It formulates the problem of polyp detection as a probability calculation through a unified Bayesian statistical model. The polyp likelihood is modeled with a combination of shape and intensity features. A second principal curvature PDE provides a shape model; and partial volume effect is considered into modeling the polyp intensity distribution. The performance of the method is evaluated on a large multi-center dataset of colonic CT scans. Both qualitative and quantitative experimental results demonstrate the potential of the proposed method.

Publication Type:	Article
Additional Information:	The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-25719-3_6
Publisher Keywords:	Colon CAD; Colonic polyp detection; Bayesian framework
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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