Fully Bayesian joint model for MR brain scan tissue and structure segmentation

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

Volumn 5242 LNCS, Issue PART 2, 2008, Pages 1066-1074

  Scherrer, Benoit ^a,c,d   Forbes, Florence ^b,d   Garbay, Catherine ^c,d   Dojat, Michel ^a,d  

^a GRENOBLE INSTITUT DES NEUROSCIENCES   (France)

^b INRIA   (France)

^c CNRS   (France)

^d UNIV GRENOBLE ALPES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE SEGMENTATION; MAGNETORHEOLOGICAL FLUIDS; MARKOV PROCESSES; MEDICAL COMPUTING; MEDICAL IMAGING; STRUCTURAL FRAMES;

INTENSITY DISTRIBUTION; JOINT MODELING; LOCAL ESTIMATION; MARKOV RANDOM FIELD MODELS; MODEL PARAMETERS; STRUCTURE SEGMENTATION; SUBCORTICAL STRUCTURES; THEORETICAL FRAMEWORK;

TISSUE;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BIOLOGICAL MODEL; BRAIN; COMPUTER ASSISTED DIAGNOSIS; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; BAYES THEOREM; BRAIN; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 79551689255     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85990-1_128     Document Type: Conference Paper

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