Detection of Gad-enhancing lesions in multiple sclerosis using conditional random fields

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

Volumn 6363 LNCS, Issue PART 3, 2010, Pages 41-48

  Karimaghaloo, Zahra ^a   Shah, Mohak ^a,c   Francis, Simon J ^b   Arnold, Douglas L ^c   Collins, D Louis ^b   Arbel, Tal ^a  

^a MCGILL UNIVERSITY   (Canada)

^b MCGILL UNIVERSITY   (Canada)

^c NEURORX RESEARCH   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CLINICAL DATA; CLOSE PROXIMITY; CONDITIONAL RANDOM FIELD; CONTRAST AGENT; CURRENT TECHNIQUES; FALSE POSITIVE; LINEAR DISCRIMINANT ANALYSIS; MAGNETIC RESONANCE IMAGES; MARKOV RANDOM FIELD; MULTIPLE SCLEROSIS; NEW APPROACHES; QUANTITATIVE RESULT;

BLOOD VESSELS; CLASSIFIERS; DISCRIMINANT ANALYSIS; GADOLINIUM; IMAGE SEGMENTATION; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MEDICAL COMPUTING;

MEDICAL IMAGING;

CONTRAST MEDIUM; DIAGNOSTIC AGENT; GADOLINIUM;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MULTIPLE SCLEROSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS;

ALGORITHMS; BRAIN; CONTRAST MEDIA; DATA INTERPRETATION, STATISTICAL; GADOLINIUM; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MULTIPLE SCLEROSIS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84861912480     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15711-0_6     Document Type: Conference Paper

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