Gaussian mixture models and model selection for [18F] fluorodeoxyglucose positron emission tomography classification in Alzheimer's disease
PLoS one. Bd. 10. H. 4. Lawrence, Kan.: PLoS 2015 e0122731
Erscheinungsjahr: 2015
ISBN/ISSN: 1932-6203
Publikationstyp: Zeitschriftenaufsatz
Sprache: Englisch
Doi/URN: 10.1371/journal.pone.0122731
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Inhaltszusammenfassung
We present a method to discover discriminative brain metabolism patterns in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, facilitating the clinical diagnosis of Alzheimer’s disease. In the work, the term “pattern” stands for a certain brain region that characterizes a target group of patients and can be used for a classification as well as interpretation purposes. Thus, it can be understood as a so-called “region of interest (ROI)”. In the literature, an ROI is often foun...We present a method to discover discriminative brain metabolism patterns in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, facilitating the clinical diagnosis of Alzheimer’s disease. In the work, the term “pattern” stands for a certain brain region that characterizes a target group of patients and can be used for a classification as well as interpretation purposes. Thus, it can be understood as a so-called “region of interest (ROI)”. In the literature, an ROI is often found by a given brain atlas that defines a number of brain regions, which corresponds to an anatomical approach. The present work introduces a semi-data-driven approach that is based on learning the characteristics of the given data, given some prior anatomical knowledge. A Gaussian Mixture Model (GMM) and model selection are combined to return a clustering of voxels that may serve for the definition of ROIs. Experiments on both an in-house dataset and data of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) suggest that the proposed approach arrives at a better diagnosis than a merely anatomical approach or conventional statistical hypothesis testing.» weiterlesen» einklappen
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Informatik