A Bayesian approach for false positive reduction in CTC CAD

Ye, Xujiong and Beddoe, Gareth and Slabaugh, Greg (2010) A Bayesian approach for false positive reduction in CTC CAD. In: Virtual Colonoscopy and Abdominal Imaging. Computational Challenges and Clinical Opportunities - Second International Workshop, held in conjunction with MICCAI 2010 , September 20-24, 2010, Beijing, China.

Full content URL: http://dx.doi.org/10.1007/978-3-642-25719-3_6

Documents
A_Bayesian_Approach_for_False_Positive_Reduction_in_miccai_workshop_2009.pdf

Request a copy
[img] PDF
A_Bayesian_Approach_for_False_Positive_Reduction_in_miccai_workshop_2009.pdf
Restricted to Repository staff only

178kB
Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

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.

Additional Information: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.
Keywords:colon CAD, colonic polyp detection, Bayesian framework
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:7316
Deposited On:23 Jan 2013 15:36

Repository Staff Only: item control page