| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 416309 | Computational Statistics & Data Analysis | 2006 | 17 Pages |
Abstract
We present a statistically innovative as well as scientifically and practically relevant method for automatically segmenting magnetic resonance images using hierarchical mixture models. Our method is a general tool for automated cortical analysis which promises to contribute substantially to the science of neuropsychiatry. We demonstrate that our method has advantages over competing approaches on a magnetic resonance brain imagery segmentation task.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Carey E. Priebe, Michael I. Miller, J. Tilak Ratnanather,