A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS)

Medical Physics

Volumn 36, Issue 9, 2009, Pages 3927-3939

  Tiwari, Pallavi ^a   Rosen, Mark ^b   Madabhushi, Anant ^a  

^a RUTGERS UNIVERSITY   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Computer aided diagnosis; Hierarchical clustering; K means; Locally linear embedding; Magnetic resonance spectroscopy; Nonlinear dimensionality reduction; Principal component analysis; Prostate cancer; Replicated clustering; Unsupervised classification

Indexed keywords

BIOMOLECULES; CALCIUM COMPOUNDS; COMPUTER AIDED ANALYSIS; COMPUTER AIDED DIAGNOSIS; DIMENSIONALITY REDUCTION; DISEASES; HIERARCHICAL CLUSTERING; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; MAGNETIC RESONANCE SPECTROSCOPY; MAGNETISM; METABOLITES; NONLINEAR ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; REDUCTION; RESONANCE; SIGNAL TO NOISE RATIO; SPECTRUM ANALYSIS; UROLOGY;

K-MEANS; LOCALLY LINEAR EMBEDDING; NONLINEAR DIMENSIONALITY REDUCTION; PROSTATE CANCERS; REPLICATED CLUSTERING; UNSUPERVISED CLASSIFICATION;

K-MEANS CLUSTERING;

ALGORITHM; ARTICLE; CANCER DIAGNOSIS; CANCER LOCALIZATION; COMPUTER AIDED DESIGN; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; EVALUATION; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PRIORITY JOURNAL; PROSTATE CANCER; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; RATING SCALE; SENSITIVITY AND SPECIFICITY; TUMOR VOLUME; VALIDATION STUDY;

ALGORITHMS; CLUSTER ANALYSIS; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; LINEAR MODELS; MAGNETIC RESONANCE SPECTROSCOPY; MALE; NONLINEAR DYNAMICS; PILOT PROJECTS; PROBABILITY; PROSTATE; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 69549088239     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.3180955     Document Type: Article

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