Algorithm:Past Featured Articles

• K. M. Pohl et al. A Bayesian Model for Joint Segmentation and 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.

K. M. Pohl, J. Fisher, W.E.L. Grimson, R. Kikinis, and W.M. Wells, A Bayesian Model for Joint Segmentation and Registration. NeuroImage, 31(1):228-239, 2006.

• D. Nain et al.

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.

K. M. Pohl, J. Fisher, W.E.L. Grimson, R. Kikinis, and W.M. Wells, A Bayesian Model for Joint Segmentation and Registration. NeuroImage, 31(1):228-239, 2006.