We propose a novel method to segment multi-channel magnetic resonance brain images into tissue classes taking into consideration the bias fields created by in-homogeneities of the scanners. The joint probability of tissue intensities in the multi-channel image is modeled using a multivariate Gaussian function; the prior models of tissue classes are presumed to be Markov random fields. An iterative algorithm is proposed to find the maximum a posteriori estimation of segmentation; suboptimally. Experiments on simultaneously acquired proton-density and T/sub 2/-weighted images are demonstrated.
Tissue segmentation of multi-channel brain images with inhomogeneity correction
2003-01-01
427859 byte
Conference paper
Electronic Resource
English
Tissue Segmentation of Multi-Channel Brain Images with Inhomogeneity Correction
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