A Bayesian model of shape and appearance for subcortical brain segmentation

NeuroImage

Volumn 56, Issue 3, 2011, Pages 907-922

  Patenaude, Brian ^a,b   Smith, Stephen M ^a   Kennedy, David N ^c   Jenkinson, Mark ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF MASSACHUSETTS MEDICAL SCHOOL   (United States)

Author keywords

Bayesian; Classification; Segmentation; Shape model; Subcortical structures

Indexed keywords

ADOLESCENT; ADULT; AGED; ALZHEIMER DISEASE; ANALYSIS OF COVARIANCE; ARTICLE; AUTOANALYSIS; BAYES THEOREM; BRAIN CORTEX; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; DATA ANALYSIS SOFTWARE; DISPLAY SYSTEM; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE QUALITY; IMAGE SEGMENTATION; IMAGING AND DISPLAY; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NEUROANATOMY; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; STRUCTURE ANALYSIS; TISSUE DISTRIBUTION; VOXEL BASED MORPHOMETRY;

ADOLESCENT; ADULT; AGED; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BRAIN; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LINEAR MODELS; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; REPRODUCIBILITY OF RESULTS; THALAMUS; YOUNG ADULT;

EID: 79955484518     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2011.02.046     Document Type: Article

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