Novel morphometric based classification via diffeomorphic based shape representation using manifold learning

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 658-665

  Sparks, Rachel ^a   Madabhushi, Anant ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANATOMICAL STRUCTURES; AREA RATIOS; BREAST LESION; CLINICAL PROBLEMS; CLUSTER-BASED; COMPUTERIZED DECISION; DCE-MRI; DIAGNOSTIC INFORMATION; DISEASE DIAGNOSIS; GRAPH EMBEDDINGS; MALIGNANT LESION; MANIFOLD LEARNING; MEDIAL AXIS; NONLINEAR DEPENDENCIES; PROSTATE CANCERS; REGISTRATION SCHEME; SHAPE REPRESENTATION;

CLASSIFIERS; DECISION SUPPORT SYSTEMS; DIAGNOSIS; DISEASES; MAGNETIC RESONANCE IMAGING; MEDICAL COMPUTING; MEDICAL IMAGING; MORPHOLOGY;

FEATURE EXTRACTION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; DIFFUSION WEIGHTED IMAGING; HUMAN; IMAGE ENHANCEMENT; MALE; METHODOLOGY; PATHOLOGY; PROSTATE; PROSTATE TUMOR; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; PATTERN RECOGNITION, AUTOMATED; PROSTATE; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84856228103     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15711-0_82     Document Type: Conference Paper

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