Abstract
The problems of segmentation and registration are traditionally approached individually, yet the accuracy of one is of great importance in influencing the success of the other. We aim to show that more accurate and robust results may be obtained through seeking a joint solution to these linked processes. The outlined approach applies Markov random fields in the solution of a Maximum a Posteriori model of segmentation and registration. The approach is applied to synthetic and real MRI data.
Cite
CITATION STYLE
Wyatt, P. P., & Noble, J. A. (2002). MAP MRF joint segmentation and registration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2488, pp. 580–587). Springer Verlag. https://doi.org/10.1007/3-540-45786-0_72
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