Supervised pattern recognition for the prediction of contrast-enhancement appearance in brain tumors from multivariate magnetic resonance imaging and spectroscopy

Artificial Intelligence in Medicine

Volumn 43, Issue 1, 2008, Pages 61-74

  Lee, Michael C ^a   Nelson, Sarah J ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Feature selection; Genetic algorithm; Glioma; k NN; MRI; Spectroscopy

Indexed keywords

FEATURE EXTRACTION; GENETIC ALGORITHMS; MAGNETIC RESONANCE IMAGING; SUPERVISED LEARNING;

BRAIN TUMORS; CONTRAST ENHANCEMENT;

TUMORS;

ADULT; AGED; ARTICLE; BRAIN TUMOR; CLINICAL ARTICLE; DIAGNOSTIC ACCURACY; DIFFUSION WEIGHTED IMAGING; FEMALE; GENETIC ALGORITHM; GLIOBLASTOMA; HUMAN; HUMAN TISSUE; MALE; MULTIVARIATE ANALYSIS; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PRIORITY JOURNAL; RADIODIAGNOSIS; SENSITIVITY AND SPECIFICITY; STATISTICAL SIGNIFICANCE;

ADULT; AGED; ALGORITHMS; BRAIN NEOPLASMS; CONTRAST MEDIA; FEMALE; GLIOBLASTOMA; HUMANS; IMAGE ENHANCEMENT; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; SENSITIVITY AND SPECIFICITY; SPECTRUM ANALYSIS;

EID: 42749093428     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2008.03.002     Document Type: Article

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