Multiple Sclerosis Lesion Segmentation Using Statistical and Topological Atlases
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/1442
This paper presents a new fully automatic method for segmentation of
brain images that possess multiple sclerosis (MS) lesions.
Multichannel magnetic resonance images are used to delineate multiple
sclerosis lesions while segmenting the brain into its major
structures. The method is an atlas based segmentation
technique employing a topological atlas as well as a statistical
atlas. An advantage of this approach is that all segmented structures
are topologically constrained, thereby allowing subsequent processing
with cortical unfolding or diffeomorphic shape analysis techniques.
Validation on data from two studies demonstrates that the method has
an accuracy comparable with other MS lesion segmentation methods,
while simultaneously segmenting the whole brain.

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plus MS lesion segmentation via topologically constrained tissue classification by Martin Styner on 07-29-2008 for revision #2
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plus Review by Simon Warfield on 07-25-2008 for revision #2
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Categories: Atlas-based segmentation, Iterative clustering, Segmentation, Unsupervised learning and clustering
Keywords: Multiple Sclerosis, Tissue Classification, Magnetic Resonance Imaging
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