2011 | OriginalPaper | Buchkapitel
A Bayesian Approach for False Positive Reduction in CTC CAD
verfasst von : Xujiong Ye, Gareth Beddoe, Greg Slabaugh
Erschienen in: Virtual Colonoscopy and Abdominal Imaging. Computational Challenges and Clinical Opportunities
Verlag: Springer Berlin Heidelberg
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This paper presents an automated detection method for identifying colonic polyps and reducing 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 the partial volume effect is considered in modeling of the polyp intensity distribution. The performance of the method was evaluated on a large multi-center dataset of colonic CT scans. Both qualitative and quantitative experimental results demonstrate the potential of the proposed method.