Automated extraction of the cortical sulci based on a supervised learning approach

IEEE Transactions on Medical Imaging

Volumn 26, Issue 4, 2007, Pages 541-552

  Tu, Zhuowen ^a   Zheng, Songfeng ^b   Yuille, Alan L ^b   Reiss, Allan L ^c   Dutton, Rebecca A ^a   Lee, Agatha D ^a   Galaburda, Albert M ^d   Dinov, Ivo ^a   Thompson, Paul M ^a   Toga, Arthur W ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d HARVARD MEDICAL SCHOOL   (United States)

Author keywords

Cortical sulci; Discriminative models; Dynamic programming; Learning; Magnetic resonance (MR) images; Probability boosting tree

Indexed keywords

ALGORITHMS; DECISION TREES; DYNAMIC PROGRAMMING; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING; MATHEMATICAL MODELS; MEDICAL IMAGING; PROBABILISTIC LOGICS;

CORTICAL SULCI; DISCRIMINATIVE MODELS; PROBABILITY BOOSTING TREE; PROBABILITY MAPS;

FEATURE EXTRACTION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN CORTEX; COMPUTER ASSISTED DIAGNOSIS; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; MATHEMATICAL COMPUTING; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CEREBRAL CORTEX; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 34047184238     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2007.892506     Document Type: Article

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