Graph run-length matrices for histopathological image segmentation

IEEE Transactions on Medical Imaging

Volumn 30, Issue 3, 2011, Pages 721-732

  Tosun, Akif Burak ^a   Gunduz Demir, Cigdem ^a  

^a BILKENT UNIVERSITY   (Turkey)

Author keywords

Cancer; graphs; histopathological image analysis; image segmentation; image texture analysis; perceptual image segmentation

Indexed keywords

CANCER; GRAPHS; HISTOPATHOLOGICAL IMAGES; IMAGE TEXTURE ANALYSIS; PERCEPTUAL IMAGE SEGMENTATION;

ALGORITHMS; DISEASES; IMAGE ANALYSIS; TEXTURES; TISSUE;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COLON; COLORECTAL TUMOR; COMPUTER ASSISTED DIAGNOSIS; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MICROSCOPY; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COLON; COLORECTAL NEOPLASMS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MICROSCOPY; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 79952174587     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2010.2094200     Document Type: Article

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