Abstract

We describe a fully automated method for model-based tissue classification of magnetic resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single- and multispectral MR images, corrects for MR signal inhomogeneities, and incorporates contextual information by means of Markov random Fields (MRF's). A digital brain atlas containing prior expectations about the spatial location of tissue classes is used to initialize the algorithm. This makes the method fully automated and therefore it provides objective and reproducible segmentations. We have validated the technique on simulated as well as on real MR images of the brain.

Keywords

Artificial intelligenceComputer scienceComputer visionMedical imagingImage segmentationPattern recognition (psychology)Image (mathematics)

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Publication Info

Year
1999
Type
article
Volume
18
Issue
10
Pages
897-908
Citations
1025
Access
Closed

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Cite This

Koen Van Leemput, Frederik Maes, Dirk Vandermeulen et al. (1999). Automated model-based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging , 18 (10) , 897-908. https://doi.org/10.1109/42.811270

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DOI
10.1109/42.811270