Spatial decision forests for MS lesion segmentation in multi-channel MR images

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

Volumn 6361 LNCS, Issue PART 1, 2010, Pages 111-118

  Geremia, Ezequiel ^a,c   Menze, Bjoern H ^a,b   Clatz, Olivier ^a   Konukoglu, Ender ^c   Criminisi, Antonio ^c   Ayache, Nicholas ^a  

^a INRIA   (France)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c MICROSOFT RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC SEGMENTATIONS; DATA SETS; DECISION FOREST; MR IMAGES; MR INTENSITY; MULTI-CHANNEL; MULTIPLE SCLEROSIS LESIONS; PROBABILISTIC CLASSIFICATION; QUANTITATIVE EVALUATION; SPATIAL PRIORS; STATE OF THE ART; SYMMETRY FEATURES;

D REGION; IMAGE SEGMENTATION; LEARNING ALGORITHMS; MEDICAL COMPUTING; MEDICAL IMAGING;

THREE DIMENSIONAL;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; DECISION SUPPORT SYSTEM; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MULTIPLE SCLEROSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; DECISION SUPPORT TECHNIQUES; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; MULTIPLE SCLEROSIS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 79958012408     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15705-9_14     Document Type: Conference Paper

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