title: Detection and segmentation of colonic polyps on implicit isosurfaces by second principal curvature flow.
abstract: Today's computer aided detection systems for computed tomography colonography (CTC) enable automated detection and segmentation of colorectal polyps. We present a paradigm shift by proposing a method that measures the amount of protrudedness of a candidate object in a scale adaptive fashion. One of the main results is that the performance of the candidate detection depends only on one parameter, the amount of protrusion. Additionally the method yields correct polyp segmentation without the need of an additional segmentation step. The supervised pattern recognition involves a clear distinction between size related features and features related to shape or intensity. A Mahalanobis transformation of the latter facilitates ranking of the objects using a logistic classifier. We evaluate two implementations of the method on 84 patients with a total of 57 polyps larger than or equal to 6 mm. We obtained a performance of 95% sensitivity at four false positives per scan for polyps larger than or equal to 6 mm.
authors.affiliation: Quantitative Imaging Group, Delft University of Technology, NL-2628 CJ Delft, The Netherlands.
authors.forename: Cees
authors.initials: C
authors.lastname: van Wijk
authors.forename: Vincent F
authors.initials: VF
authors.lastname: van Ravesteijn
authors.forename: Frans M
authors.initials: FM
authors.lastname: Vos
authors.forename: Lucas J
authors.initials: LJ
authors.lastname: van Vliet
mesh_terms: D000465:Algorithms; D003111:Colonic Polyps / Q000175:diagnosis* / Q000473:pathology; D023881:Colonography, Computed Tomographic / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve