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