Fast and robust 3-D MRI brain structure segmentation

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 575-583

  Wels, Michael ^a,d   Zheng, Yefeng ^b   Carneiro, Gustavo ^c   Huber, Martin ^d   Hornegger, Joachim ^a   Comaniciu, Dorin ^b  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b SIEMENS CORPORATE RESEARCH   (United States)

^c INSTITUTE FOR SYSTEMS AND ROBOTICS   (Portugal)

^d SIEMENS AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ANNOTATED TRAINING DATA; AUTOMATIC DETECTION; BRAIN MR; BRAIN SCAN; BRAIN STRUCTURE; CONTEXTUAL INFORMATION; DISCRIMINATIVE MODELS; GOLD STANDARDS; GRAY MATTER; HAAR FEATURES; HIGH-DIMENSIONAL; HUMAN BRAIN; LEVEL OF ABSTRACTION; MAGNETIC RESONANCE IMAGES; MARGINAL SPACE LEARNING; NOVEL METHODS; PARAMETER INVARIANCE; PARAMETER SPACES; POSSIBLE SOLUTIONS; SHAPE PARAMETERS; STATE-OF-THE-ART APPROACH; TOPDOWN;

ABSTRACTING; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; MEDICAL COMPUTING; RESONANCE; SCANNING;

THREE DIMENSIONAL;

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

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 79956061031     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04271-3_70     Document Type: Conference Paper

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