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