dc.date.accessioned | 2005-12-22T02:25:35Z | |
dc.date.accessioned | 2018-11-24T10:24:26Z | |
dc.date.available | 2005-12-22T02:25:35Z | |
dc.date.available | 2018-11-24T10:24:26Z | |
dc.date.issued | 2005-04-01 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/30532 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/30532 | |
dc.description.abstract | 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. | |
dc.format.extent | 13 p. | |
dc.format.extent | 18741928 bytes | |
dc.format.extent | 826219 bytes | |
dc.language.iso | en_US | |
dc.subject | AI | |
dc.subject | Expectation Maximization | |
dc.subject | Segmentation | |
dc.subject | Registration | |
dc.subject | Medical Image Analysis | |
dc.title | An Expectation Maximization Approach for Integrated Registration, Segmentation, and Intensity Correction | |