A unifying approach to registration, segmentation, and intensity correction

Citation:

Pohl KM, Fisher J, Levitt JJ, Shenton ME, Kikinis R, Grimson EWL, Wells WM. A unifying approach to registration, segmentation, and intensity correction. Med Image Comput Comput Assist Interv. 2005;8 (Pt 1) :310-8.

Date Published:

2005

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.
Last updated on 01/24/2017