Magnetic resonance image analysis by information theoretic criteria and stochastic site models

IEEE Transactions on Information Technology in Biomedicine

Volumn 5, Issue 2, 2001, Pages 150-158

  Wang, Yue ^a,b,c   Adah, Tülay ^a,d   Xuan, Jianhua ^d   Szabo, Zsolt ^c  

^a IEEE   (United States)

^b Catholic University of America ^*  (United States)

^c DEPARTMENT OF PATHOLOGY   (United States)

^d University of Marylandw Baltimore County   (United States)

Author keywords

Finite normal mixture; Image segmentation; Information theoretic criteria; Patient site model; Tissue quantification

Indexed keywords

EXPECTATION MINIMIZATION (EM); TISSUE QUANTIFICATION;

ALGORITHMS; COMPUTER SIMULATION; DIAGNOSIS; IMAGE ANALYSIS; IMAGE SEGMENTATION; MARKOV PROCESSES; MATHEMATICAL MODELS; TISSUE;

MAGNETIC RESONANCE IMAGING;

ALGORITHM; ARTICLE; BRAIN; HISTOLOGY; HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; STATISTICAL MODEL;

ALGORITHMS; BRAIN; HUMANS; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL;

EID: 0035354253     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/4233.924805     Document Type: Article

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