A Bayesian Approach for False Positive Reduction in CTC CAD

Ye, X., Beddoe, G. & Slabaugh, G.G. (2011). A Bayesian Approach for False Positive Reduction in CTC CAD. Lecture Notes in Computer Science, 6668, pp. 40-46. doi: 10.1007/978-3-642-25719-3_6

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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.

Item Type: Article
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-25719-3_6
Uncontrolled Keywords: Colon CAD; Colonic polyp detection; Bayesian framework
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine
Divisions: School of Informatics > Department of Computing
URI: http://openaccess.city.ac.uk/id/eprint/6336

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