Journal
NEUROIMAGE
Volume 31, Issue 1, Pages 228-239Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2005.11.044
Keywords
registration; segmentation; subcortical segmentation; Bayesian modeling; expectation-maximization
Funding
- NCRR NIH HHS [P41 RR013218, U24-RR-021382, P41-RR-13218] Funding Source: Medline
- NIBIB NIH HHS [U54-EB-005149] Funding Source: Medline
- NINDS NIH HHS [R01-NS-051826] Funding Source: Medline
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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 rind 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. (c) 2005 Elsevier Inc. All rights reserved.
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