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