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

Til Aach; Herbert Dawid

doi:10.1117/12.24256

1 September 1990 Region-oriented 3-D segmentation of NMR datasets: a statistical model-based approach

Til Aach, Herbert Dawid

Proceedings Volume 1360, Visual Communications and Image Processing '90: Fifth in a Series; (1990) https://doi.org/10.1117/12.24256
Event: Visual Communications and Image Processing '90, 1990, Lausanne, Switzerland

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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Til Aach and Herbert Dawid "Region-oriented 3-D segmentation of NMR datasets: a statistical model-based approach", Proc. SPIE 1360, Visual Communications and Image Processing '90: Fifth in a Series, (1 September 1990); https://doi.org/10.1117/12.24256

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