Region-oriented 3-D segmentation of NMR datasets: a statistical model-based approach

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Abstract
We present a three-stage method for segmenting NMR-datasets of the head into 3D-regions corresponding to different brain matter classes, liquid containing structures, cranium, and background. Our technique works from the beginning with 3D-regions, whose internal ’grey’ values as well as shapes are described by stochastic models. The first phase starts by assuming the entire dataset as consisting of only one region, and then recursively extracts those areas which are not compatible with this hypothesis. During this step, special emphasis is given to the problem of accurately locating the region surfaces. In the second stage, a Bayes classifier groups the regions into different categories, like brain matter, liquid, cranium, and background. Classification errors are corrected largely automatically during the third stage by applying simple knowledge about the topological relationships between the classes.

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