Intensity standardization simplifies brain MR image segmentation

Computer Vision and Image Understanding

Volumn 113, Issue 10, 2009, Pages 1095-1103

  Zhuge, Ying ^a   Udupa, Jayaram K ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Brain image segmentation; Fuzzy connectedness; Inhomogeneity correction; MRI; Standardization

Indexed keywords

BRAIN IMAGE SEGMENTATION; BRAIN MR; BRAIN MR IMAGE SEGMENTATION; BRAIN SEGMENTATION; BRAIN TISSUE; COMPUTATIONAL COSTS; DATA SETS; EXPECTATION-MAXIMIZATION METHOD; FINITE MIXTURES; FUZZY CONNECTEDNESS; FUZZY MEMBERSHIP VALUES; GAUSSIAN MODEL; INHOMOGENEITIES; INHOMOGENEITY CORRECTION; INTENSITY DISTRIBUTION; INTENSITY STANDARDIZATION; INTENSITY VARIATIONS; K-NEAREST NEIGHBORS; MAXIMUM LIKELIHOOD CRITERIA; MEAN INTENSITY; MODEL-BASED; MORPHOLOGICAL OPERATIONS; MR IMAGES; MRI; MULTIPLE SCLEROSIS; NON-ITERATIVE METHOD; PATIENT DATA; PREPROCESSING TECHNIQUES; QUANTITATIVE COMPARISON; SPATIAL CONSTRAINTS; TRAINING DATA SETS;

BLIND SOURCE SEPARATION; BRAIN; COVARIANCE MATRIX; DIGITAL IMAGE STORAGE; FUZZY SYSTEMS; HOSPITAL DATA PROCESSING; IMAGE SEGMENTATION; MATHEMATICAL MORPHOLOGY; MAXIMUM LIKELIHOOD; SONOCHEMISTRY; STANDARDIZATION; TISSUE;

ITERATIVE METHODS;

EID: 69549135240     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2009.06.003     Document Type: Article

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