Learning the topological properties of brain tumors

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 2, Issue 3, 2005, Pages 262-269

  Demir, Cigdem ^a   Gultekin, S Humayun ^b,c   Yener, Bülent ^a,d,e,f,g  

^a Rensselaer Polytechnic Institute   (United States)

^b MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^c OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

^d Department of Computer Science ^*  (United States)

^e RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^f Griffiss Institute   (United States)

^g IEEE   (United States)

Author keywords

Graph theory; Image representation; Machine learning; Medical information systems; Model development

Indexed keywords

CELLULAR DENSITY; IMAGE REPRESENTATION; MEDICAL INFORMATION SYSTEMS; MODEL DEVELOPMENT;

BRAIN; CELLS; GRAPH THEORY; INFORMATION ANALYSIS; METRIC SYSTEM; TISSUE;

TUMORS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN TUMOR; CLUSTER ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; EVALUATION; HUMAN; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; VALIDATION STUDY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN NEOPLASMS; CLUSTER ANALYSIS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 27544512013     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2005.42     Document Type: Article

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