Supervised nonparametric image parcellation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5762 LNCS, Issue PART 2, 2009, Pages 1075-1083

  Sabuncu, Mert R ^a   Yeo, B T Thomas ^a   Van Leemput, Koen ^a,b,c   Fischl, Bruce ^a,b   Golland, Polina ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN MRI; COMPUTATIONAL BURDEN; EXPECTATION MAXIMIZATION; IMAGE ALIGNMENT; JOINT DISTRIBUTIONS; LABEL IMAGES; LABELED TRAINING DATA; MEDICAL IMAGES; MULTIPLE REGIONS; NON-PARAMETRIC; STATE-OF-THE-ART ALGORITHMS; SUBCORTICAL STRUCTURES; SUPERVISED LEARNING PROBLEMS; TRAINING DATA; WHITE MATTER;

CHANNEL ESTIMATION; LABELS; MEDICAL COMPUTING;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MYELINATED NERVE; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; ULTRASTRUCTURE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; NERVE FIBERS, MYELINATED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 77952269978     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04271-3_130     Document Type: Conference Paper

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