Learning-based segmentation framework for tissue images containing gene expression data

IEEE Transactions on Medical Imaging

Volumn 26, Issue 5, 2007, Pages 728-744

  Bello, Musodiq ^a,b   Ju, Tao ^c   Carson, James ^d   Warren, Joe ^e   Chiu, Wah ^f   Kakadiaris, Ioannis A ^a,g  

^a IEEE   (United States)

^b UNIVERSITY OF HOUSTON   (United States)

^c WASHINGTON UNIVERSITY IN ST LOUIS   (United States)

^d PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

^e Rice University   (United States)

^f BAYLOR COLLEGE OF MEDICINE   (United States)

^g University of Houston   (United States)

Author keywords

Feature selection; Gene expression; Segmentation; Shape modeling; Texture classification

Indexed keywords

AUTOMATIC METHODS; SHAPE MODELING; TEXTURE CLASSIFICATION;

BRAIN; CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; GENE EXPRESSION; IMAGE SEGMENTATION; LEARNING SYSTEMS; STATISTICAL METHODS;

TISSUE;

NERVE PROTEIN;

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; C57BL MOUSE; COMPUTER ASSISTED DIAGNOSIS; CYTOLOGY; EVALUATION; GENE EXPRESSION PROFILING; IMAGE ENHANCEMENT; METABOLISM; METHODOLOGY; MOUSE; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING; TISSUE DISTRIBUTION;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; BRAIN; GENE EXPRESSION PROFILING; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MICE; MICE, INBRED C57BL; NERVE TISSUE PROTEINS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TISSUE DISTRIBUTION;

EID: 34247578273     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2007.895462     Document Type: Article

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