Abstract
A Bayesian, model-based method for segmentation of Magnetic Resonance images is proposed. A discrete vector valued Markov Random Field model is used as a regularizing prior in a Bayesian classification algorithm to minimize the effect of salt-and-pepper noise common in clinical scans. The continuous Mean Field solution to the MRP is recovered using an Expectation-Maximization algorithm, and is a probabilistic segmentation of the image. A separate model is used to encode the relative geometry of structures, and as a spatially varying prior in the Bayesian classifier. Preliminary results are presented for the segmentation of white matter, gray matter, fluid, and fat in Gradient Echo MR images of the brain.
The authors would like to thank Martha Shenton for contributing data for this paper. W. Wells received support for this research in part from NIMH Research Scientist Development Awards K02 MH-01110 and R29 MH-50747 (Martha Shenton, PI) and from a Whitaker Foundation Biomedical Research Grant (W. Wells, PI). R. Kikinis received partial support from: NIH: RO1 CA 46627-08, PO1 CA67165-01A1, PO1 AG04953-14, NSF: BES 9631710 and Darpa: F41624-96-2-0001.
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Kapur, T., Eric, W., Grimson, L., Kikinis, R., Wells, W.M. (1998). Enhanced spatial priors for segmentation of magnetic resonance imagery. In: Wells, W.M., Colchester, A., Delp, S. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI’98. MICCAI 1998. Lecture Notes in Computer Science, vol 1496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056231
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DOI: https://doi.org/10.1007/BFb0056231
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