Medial-based deformable models in nonconvex shape-spaces for medical image segmentation

IEEE Transactions on Medical Imaging

Volumn 31, Issue 1, 2012, Pages 33-50

  McIntosh, Chris ^a   Hamarneh, Ghassan ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

Author keywords

Deformable models; evolutionary computing; genetic algorithms; medial shape representation; medical image segmentation

Indexed keywords

BRAIN MAGNETIC RESONANCE IMAGES; COMBINATORIAL OPTIMIZATION TECHNIQUES; CORPUS CALLOSUM; DEFORMABLE MODELS; EVOLUTIONARY COMPUTING; GLOBALOPTIMUM; GRADIENT DESCENT; GRADIENT-BASED OPTIMIZERS; GRAPH-THEORETIC; LOCAL MINIMUMS; MEDIAL-SHAPE REPRESENTATION; MEDICAL IMAGE SEGMENTATION; MODEL INITIALIZATION; NONCONVEX; OPTIMIZATION TECHNIQUES; OPTIMIZERS; POSE ESTIMATION; SHAPE CHANGE; SHAPE STATISTICS;

COMBINATORIAL OPTIMIZATION; DEFORMATION; GENETIC ALGORITHMS; GRAPH THEORY; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING; OPTIMIZATION;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; CORPUS CALLOSUM; HISTOLOGY; HUMAN; IMAGE PROCESSING; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CORPUS CALLOSUM; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, GENETIC; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS;

EID: 84855398203     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2011.2162528     Document Type: Article

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