A conditional random field approach for coupling local registration with robust tissue and 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 540-548

  Scherrer, Benoit ^a,c   Forbes, Florence ^b,c   Dojat, Michel ^a,c  

^a GRENOBLE INSTITUT DES NEUROSCIENCES   (France)

^b INRIA   (France)

^c UNIV GRENOBLE ALPES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE SEGMENTATION; MEDICAL COMPUTING; MEDICAL IMAGING; RANDOM PROCESSES; TISSUE;

ATLAS REGISTRATION; COMPLEX JOINTS; CONDITIONAL RANDOM FIELD; ESTIMATION SCHEMES; LOCAL REGISTRATION; MODEL COMPONENTS; MODELLING STRATEGIES; STRUCTURE SEGMENTATION;

TISSUE ENGINEERING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; INFORMATION RETRIEVAL; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; INFORMATION STORAGE AND RETRIEVAL; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 84883846956     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04271-3_66     Document Type: Conference Paper

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