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An Expectation Maximization Approach for Integrated Registration, Segmentation, and Intensity Correction

This paper presents a statistical framework which combines the registration of an atlas with the segmentation of MR images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image inhomogeneities, anatomical labelmap, and a mapping from the atlas to the image space. An example of the approach is given for a brain structure-dependent affine mapping approach. The algorithm produces high quality segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 30 brain MR images. In addition, we show that the approach performs better than similar methods which separate the registration from the segmentation problem.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/30532
Date01 April 2005
CreatorsPohl, Kilian M., Fisher, John, Grimson, W. Eric L., Wells, William M.
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
Format13 p., 18741928 bytes, 826219 bytes, application/postscript, application/pdf
RelationMassachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory

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