Segmenting magnetic resonance images via hierarchical mixture modelling.
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.
Priebe
Carey E
CE
Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA.
Miller
Michael I
MI
Ratnanather
J Tilak
JT
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