Polyps flagging in virtual colonoscopy
Computer tomographic colonography, combined with computer-aided detection, is a promising emerging technique for colonic polyp analysis. We present a complete pipeline for polyp detection, starting with a simple colon segmentation technique that enhances polyps, followed by an adaptive-scale candidate polyp delineation and classification based on new texture and geometric features that consider both the information in the candidate polyp and its immediate surrounding area. The proposed system is tested with ground truth data, including challenging flat and small polyps. For polyps larger than 6mm in size we achieve 100% sensitivity with just 0.9 false positives per case, and for polyps larger than 3mm in size we achieve 93% sensitivity with 2.8 false positives per case. © Springer-Verlag 2013.
Duke Scholars
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- Artificial Intelligence & Image Processing
- 46 Information and computing sciences
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Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences