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.

• Type: Conference
• Year: 2005
• Where: MICCAI
• Authors: Kilian M. Pohl, John W. Fisher III, James J. Levitt, Martha Elizabeth Shenton, Ron Kikinis, W. Eric L. Grimson, William M. Wells III
• Keywords: Affine Mapping Approach, Expectation Maximization-based Algorithm, Magnetic Resonance Images, Medical Imaging, MICCAI 2005