Mean-field and information-theoretic algorithms for direct segmentation of tomographic images

Ian B. Kerfoot; Yoram Bresler; Andrew S. Belmont

doi:10.1117/12.148709

29 July 1993 Mean-field and information-theoretic algorithms for direct segmentation of tomographic images

Ian B. Kerfoot, Yoram Bresler, Andrew S. Belmont

Proceedings Volume 1905, Biomedical Image Processing and Biomedical Visualization; (1993) https://doi.org/10.1117/12.148709
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1993, San Jose, CA, United States

Abstract

We apply the weak membrane model with optimization by mean field annealing to the direct segmentation of tomographic images. We also introduce models based on the minimum description length principle that include penalties for measurement error, boundary length, regions, and means. Outliers are prevented by upper and lower bound constraints on pixel values. Several models are generalized to three-dimensional images. The superiority of our models to convolution back projection is demonstrated experimentally.

Citation Download Citation

Ian B. Kerfoot, Yoram Bresler, and Andrew S. Belmont "Mean-field and information-theoretic algorithms for direct segmentation of tomographic images", Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); https://doi.org/10.1117/12.148709

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