A neural network architecture for automatic segmentation of fluorescence micrographs

Neurocomputing

Volumn 48, Issue 1-4, 2002, Pages 357-367

  Nattkemper, Tim W ^a   Wersing, Heiko ^a   Schubert, Walter ^b   Ritter, Helge ^a  

^a BIELEFELD UNIVERSITY   (Germany)

^b OTTO VON GUERICKE UNIVERSITY   (Germany)

Author keywords

Contour grouping; Fluorescence microscopy; Functional proteomics; Segmentation

Indexed keywords

APPROXIMATION THEORY; FEATURE EXTRACTION; FLUORESCENCE; IMAGE SEGMENTATION; LEARNING SYSTEMS;

FLUORESCENT MICROGRAPHS;

RECURRENT NEURAL NETWORKS;

ANALYTIC METHOD; ARTIFICIAL NEURAL NETWORK; AUTOANALYSIS; CELL SHAPE; CELL SIZE; CELLULAR DISTRIBUTION; COMPUTER GRAPHICS; COMPUTER MODEL; COMPUTER SYSTEM; CONTROLLED STUDY; EQUIPMENT DESIGN; FLUORESCENCE ANALYSIS; HUMAN; HUMAN CELL; HUMAN TISSUE; IMAGE ANALYSIS; LEARNING; LYMPHOCYTE; PRIORITY JOURNAL; REVIEW; SIGNAL NOISE RATIO; TRAINING; VISION;

EID: 0036825536     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(01)00642-7     Document Type: Review

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