Ye, Xujiong, 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 |
| ||||
![[img]](http://eprints.lincoln.ac.uk/style/images/fileicons/application_pdf.png) |
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