title: A unifying approach to registration, segmentation, and intensity correction.
abstract: We 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.
authors: Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. pohl@csail.mit.edu
authors: Kilian M
authors: KM
authors: Pohl
authors: John
authors: J
authors: Fisher
authors: James J
authors: JJ
authors: Levitt
authors: Martha E
authors: ME
authors: Shenton
authors: Ron
authors: R
authors: Kikinis
authors: W Eric L
authors: WE
authors: Grimson
authors: William M
authors: WM
authors: Wells