Fast globally optimal segmentation of cells in fluorescence microscopy images

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6891 LNCS, Issue PART 1, 2011, Pages 645-652

  Bergeest, Jan Philip ^a   Rohr, Karl ^a  

^a UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE CONTOURS; CELL NUCLEUS; CELL TYPES; ENERGY FUNCTIONALS; FLUORESCENCE MICROSCOPY IMAGES; GLOBAL SOLUTIONS; HIGH-THROUGHPUT SCREENING; LOCAL MINIMUMS; OPTIMAL SEGMENTATION; PROTEIN EXPRESSIONS; QUANTITATIVE COMPARISON; SEGMENTATION RESULTS;

CELLS; FLUORESCENCE; FLUORESCENCE MICROSCOPY; MEDICAL COMPUTING; MEDICAL IMAGING;

IMAGE SEGMENTATION;

FLUORESCENT DYE;

ALGORITHM; ANIMAL; ARTICLE; AUTOMATED PATTERN RECOGNITION; CELL NUCLEUS; CELL STRAIN 3T3; CYTOLOGY; DIAGNOSTIC IMAGING; FLUORESCENCE MICROSCOPY; IMAGE PROCESSING; METABOLISM; METHODOLOGY; MOUSE; REPRODUCIBILITY; STATISTICAL MODEL; THEORETICAL MODEL;

ALGORITHMS; ANIMALS; CELL BIOLOGY; CELL NUCLEUS; DIAGNOSTIC IMAGING; FLUORESCENT DYES; IMAGE PROCESSING, COMPUTER-ASSISTED; MICE; MICROSCOPY, FLUORESCENCE; MODELS, STATISTICAL; MODELS, THEORETICAL; NIH 3T3 CELLS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS;

EID: 82255164599     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23623-5_81     Document Type: Conference Paper

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