A unifying approach to registration, segmentation, and intensity correction

Authors:
Kilian M. Pohl;John Fisher;James J. Levitt;Martha E. Shenton;Ron Kikinis;W. Eric L. Grimson;William M. Wells
Affiliations:
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA;Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA;Surgical Planning Laboratory, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA;Surgical Planning Laboratory, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA;Surgical Planning Laboratory, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA;Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA;Surgical Planning Laboratory, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

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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.