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

dc.date.accessioned2005-12-22T02:25:35Z
dc.date.accessioned2018-11-24T10:24:26Z
dc.date.available2005-12-22T02:25:35Z
dc.date.available2018-11-24T10:24:26Z
dc.date.issued2005-04-01
dc.identifier.urihttp://hdl.handle.net/1721.1/30532
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/30532
dc.description.abstractThis 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.
dc.format.extent13 p.
dc.format.extent18741928 bytes
dc.format.extent826219 bytes
dc.language.isoen_US
dc.subjectAI
dc.subjectExpectation Maximization
dc.subjectSegmentation
dc.subjectRegistration
dc.subjectMedical Image Analysis
dc.titleAn Expectation Maximization Approach for Integrated Registration, Segmentation, and Intensity Correction


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