Fully-automated white matter hyperintensity detection with anatomical prior knowledge and without FLAIR

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

Volumn 5636 LNCS, Issue , 2009, Pages 239-251

  Schwarz, Christopher ^a   Fletcher, Evan ^b   Decarli, Charles ^b   Carmichael, Owen ^a,b  

^a University of California   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANATOMICAL PRIORS; DEPENDENCY MODEL; INFERENCE METHODS; MARKOV RANDOM FIELD; PROBABILISTIC MODELS; RUNTIMES; SPATIAL DISTRIBUTION; TEST DATA; TEST IMAGES; TRAINING EXAMPLE; WHITE MATTER;

BRAIN; DATA PROCESSING; HIDDEN MARKOV MODELS; MACHINERY; PROBABILITY DISTRIBUTIONS; STRUCTURAL FRAMES;

MEDICAL IMAGING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; DEMYELINATING DISEASE; DIFFUSE AXONAL INJURY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MYELINATED NERVE; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

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

EID: 70349315265     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02498-6_20     Document Type: Conference Paper

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