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Proceedings Paper

Analysis of kernel method for surface curvature estimation
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Paper Abstract

Surface curvature estimation is a common component of CT colonography computer-aided polyp detection algorithms. A commonly used method to compute such curvatures employs convolution kernels. We have observed situations where the kernel method produces inaccurate results that could lead to undesirable false negative and false positive polyp diagnoses. In this paper, we numerically examine this method of curvature estimation. We propose optimal choices for smoothing parameters intrinsic to the method. The proposed smoothing parameters achieve more accurate and reliable curvatures compared to those reported in the literature. Our results include responses of the system with respect to Gaussian smoothing and Gaussian noise, results on the accuracy of the curvature estimation as a function of the distance from the true surface, and examples of specific topologies of the colonic surface for which the kernel method yields inaccurate responses.

Paper Details

Date Published: 29 March 2007
PDF: 10 pages
Proc. SPIE 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images, 65112I (29 March 2007); doi: 10.1117/12.708285
Show Author Affiliations
Shannon R. Campbell, National Institutes of Health (United States)
Ronald M. Summers M.D., National Institutes of Health (United States)

Published in SPIE Proceedings Vol. 6511:
Medical Imaging 2007: Physiology, Function, and Structure from Medical Images
Armando Manduca; Xiaoping P. Hu, Editor(s)

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