A Bayesian model for joint segmentation and registration

• DOI: 10.1016/j.neuroimage.2005.11.044
• Publication Date: 2006-02-08T13:25:23Z
• Authors: Kilian M. Pohl; John Fisher; W. Eric L. Grimson; Ron Kikinis; William M. Wells
• Keywords: Image registration

A statistical model is presented 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 artifacts, anatomical labelmaps, and a structure-dependent hierarchical mapping from the atlas to the image space. The algorithm produces segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. On this set of images, the new approach performs significantly better than similar methods which sequentially apply registration and segmentation.