Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images

Cytometry Part A

Volumn 81 A, Issue 9, 2012, Pages 743-754

  Nandy, Kaustav ^a   Gudla, Prabhakar R ^a   Amundsen, Ryan ^b   Meaburn, Karen J ^c   Misteli, Tom ^c   Lockett, Stephen J ^a  

^a SAIC FREDERICK INC   (United States)

^b JOHNS HOPKINS UNIVERSITY APPLIED PHYSICS LABORATORY   (United States)

^c NATIONAL CANCER INSTITUTE   (United States)

Author keywords

Artificial neural network; Cancer diagnosis; Image analysis; Pattern classification; Tissue segmentation; Watershed

Indexed keywords

BASIC HELIX LOOP HELIX TRANSCRIPTION FACTOR; HES5 PROTEIN, HUMAN; REPRESSOR PROTEIN;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; BREAST TUMOR; CELL NUCLEUS; CHROMOSOME ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; FEMALE; FLUORESCENCE IN SITU HYBRIDIZATION; GENETICS; HUMAN; LABORATORY AUTOMATION; MAMMARY GLAND; METHODOLOGY; NUCLEAR SHAPE; PATHOLOGY;

ALGORITHMS; AUTOMATION, LABORATORY; BASIC HELIX-LOOP-HELIX TRANSCRIPTION FACTORS; BREAST NEOPLASMS; CELL NUCLEUS; CELL NUCLEUS SHAPE; CYTOGENETIC ANALYSIS; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IN SITU HYBRIDIZATION, FLUORESCENCE; MAMMARY GLANDS, HUMAN; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); REPRESSOR PROTEINS;

EID: 84865439855     PISSN: 15524922     EISSN: 15524930     Source Type: Journal    
DOI: 10.1002/cyto.a.22097     Document Type: Article

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