An Expectation Maximization Approach for Integrated Registration, Segmentation, and Intensity Correction
Author(s) • • •
Pohl, Kilian M.
Fisher, John
Grimson, W. Eric L.
Wells, William M.
Date Issued
April 1, 2005
Series/Report no.
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
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.
Subjects
AI
Expectation Maximization
Segmentation
Registration
Medical Image Analysis
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