Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field

Computerized Medical Imaging and Graphics

Volumn 33, Issue 6, 2009, Pages 431-441

  Nie, Jingxin ^a,c   Xue, Zhong ^a   Liu, Tianming ^a   Young, Geoffrey S ^b   Setayesh, Kian ^b   Guo, Lei ^c   Wong, Stephen T C ^a  

^a WEILL CORNELL MEDICAL COLLEGE   (United States)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

^c NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

Brain tumor; MRI; Segmentation

Indexed keywords

BRAIN MR; BRAIN TUMOR; BRAIN TUMOR SEGMENTATION; BRAIN TUMORS; CLINICAL APPLICATION; CLINICAL TRIAL; COREGISTRATION; DATA SETS; EXPECTATION MAXIMIZATION; HIDDEN MARKOV RANDOM FIELDS; HIGH RESOLUTION; HIGH RESOLUTION IMAGE; ISOTROPIC RESOLUTION; LOW RESOLUTION IMAGES; MR IMAGES; MRI; MULTI-CHANNEL; OPTIMIZATION PROCEDURES; PARTIAL VOLUME EFFECT; SEGMENTATION; SEGMENTATION ALGORITHMS; SPATIAL ACCURACY; SPATIAL INTERPOLATION; TISSUE SEGMENTATION; TUMOR SEGMENTATION;

ALGORITHMS; CHEMOTHERAPY; HIDDEN MARKOV MODELS; IMAGE SEGMENTATION; INTERPOLATION;

TUMORS;

ACCURACY; ALGORITHM; ARTICLE; CLINICAL ARTICLE; DATA BASE; GLIOMA; HIDDEN MARKOV MODEL; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; INTERMETHOD COMPARISON; LABORATORY AUTOMATION; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; PROCESS OPTIMIZATION; SIMULATION; VALIDATION PROCESS;

ALGORITHMS; BRAIN NEOPLASMS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MARKOV CHAINS; PATTERN RECOGNITION, AUTOMATED;

EID: 67349168722     PISSN: 08956111     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2009.04.006     Document Type: Article

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