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A Bayesian model for joint segmentation and registration.

Kilian M Pohl·John Fisher·W Eric L Grimson·Ron Kikinis·William M Wells
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Abstract

A statistical model is presented that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image artifacts, anatomical labelmaps, and a structure-dependent hierarchical mapping from the atlas to the image space. The algorithm produces segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. On this set of images, the new approach performs significantly better than similar methods which sequentially apply registration and segmentation.

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ArtifactsBayes TheoremBrainBrain DiseasesBrain MappingCerebral VentriclesDominance, CerebralHumansImage EnhancementImage Processing, Computer-AssistedMagnetic Resonance ImagingModels, StatisticalThalamusJournal ArticleResearch Support, N.I.H., ExtramuralResearch Support, U.S. Gov't, Non-P.H.S.

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Kilian M Pohl, John Fisher, W Eric L Grimson, Ron Kikinis, William M Wells. 2006-02-07. A Bayesian model for joint segmentation and registration.. https://doi.org/10.1016/j.neuroimage.2005.11.044

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