A Bayesian Model for Joint Segmentation and Registration

Citation:

Pohl KM, Fisher J, Grimson EWL, Kikinis R, Wells III WM. A Bayesian Model for Joint Segmentation and Registration. Neuroimage. 2006;31 (1) :228-39.

Date Published:

2006 May 15

Abstract:

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.

Last updated on 05/04/2017