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Proceedings Paper

Image segmentation using information theoretic criteria
Author(s): Lyndon S. Hibbard
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Paper Abstract

Image segmentations based on maximum likelihood (ML) or maximum a posteriori (MAP) analyses of object textures, edges, and shape often assume stationary Gaussian distributions for these features. For real images, neither Gaussianity nor stationarity may be realistic, so model-free inference methods would have advantages over those that are model-dependent. Relative entropy provides model-free inference, and a generalization--the Jensen-Renyi divergence (JRD)--computes optimal n-way decisions. We apply these results to patient anatomy contouring in X-ray computed tomography (CT) for radiotherapy treatment planning.

Paper Details

Date Published: 15 May 2003
PDF: 11 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.483554
Show Author Affiliations
Lyndon S. Hibbard, Computerized Medical Systems, Inc. (United States)

Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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