Detection of protrusions in curved folded surfaces applied to automated polyp detection in CT colonography

Authors:
Cees van Wijk;Vincent F. van Ravesteijn;Frank M. Vos;Roel Truyen;Ayso H. de Vries;Jaap Stoker;Lucas J. van Vliet
Affiliations:
Quantitative Imaging Group, Delft University of Technology, The Netherlands;Quantitative Imaging Group, Delft University of Technology, The Netherlands;Quantitative Imaging Group, Delft University of Technology, The Netherlands;Philips Medical Systems, Best, The Netherlands;Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands;Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands;Quantitative Imaging Group, Delft University of Technology, The Netherlands
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2006

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Abstract

Over the past years many computer aided diagnosis (CAD) schemes have been presented for the detection of colonic polyps in CT Colonography. The vast majority of these methods (implicitly) model polyps as approximately spherical protrusions. Polyp shape and size varies greatly, however and is often far from spherical. We propose a shape and size invariant method to detect suspicious regions. The method works by locally deforming the colon surface until the second principal curvature is smaller than or equal to zero. The amount of deformation is a quantitative measure of the ’protrudeness’. The deformation field allows for the computation of various additional features to be used in supervised pattern recognition. It is shown how only a few features are needed to achieve 95% sensitivity at 10 false positives (FP) per dataset for polyps larger than 6 mm.