Image segmentation using information theoretic criteria

Lyndon S. Hibbard

doi:10.1117/12.483554

15 May 2003 Image segmentation using information theoretic criteria

Lyndon S. Hibbard

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.483554
Event: Medical Imaging 2003, 2003, San Diego, California, United States

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.

Citation Download Citation

Lyndon S. Hibbard "Image segmentation using information theoretic criteria", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.483554

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