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29 April 2005 A new look at Markov random field (MRF) model-based MR image analysis
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Abstract
Pixel intensities of MR images reconstructed by Fourier Transform and Projection methods have been proved to be spatially asymptotically independent (S.A.I.) and to have exponential correlation coefficient (E.C.C.). Based on S.A.I. and E.C.C., the MR image has been proved to be embedded in an MRF with respect to a proper neighborhood system. Further, the MR image is proved to be modeled by a Finite Normal Mixture (FNM) with an MRF as its prior. A unified Expectation-Maximization (EM) algorithm is presented for performing image segmentation. S.A.I., E.C.C. and Markovianity provide means for selecting the order of neighborhood systems and the values of clique potentials. The use of the 3rd-order neighborhood system and the correlation coefficient-based assignments of clique potentials strike an optimal trade-off between good accuracy and sufficient simplicity in image segmentation.
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Tianhu Lei and Jayaram K. Udupa "A new look at Markov random field (MRF) model-based MR image analysis", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.596251
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