A Unifying Approach to Registration, Segmentation, and Intensity Correction
Pohl, Kilian M.
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CitationPohl, Kilian M., John Fisher, James J. Levitt, Martha E. Shenton, Ron Kikinis, W. Eric L. Grimson, and William M. Wells. 2005. “A Unifying Approach to Registration, Segmentation, and Intensity Correction.” Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005: 310–318. doi:10.1007/11566465_39.
AbstractWe present a statistical framework 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 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 22 magnetic resonance images. In addition, we show that the approach performs better than similar methods which separate the registration and segmentation problems.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:28538478
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