Open AccessImage analysis algorithms for cell contour recognition in budding yeast
Author(s) -
Mats Kvarnström,
Anders Logg,
Alfredo Diez,
Kristofer Bodvard,
Mikael Käll
Publication year - 2008
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.16.012943
Subject(s) - budding yeast , saccharomyces cerevisiae , fluorescence microscope , subcellular localization , protein subcellular localization prediction , microscopy , live cell imaging , yeast , artificial intelligence , image processing , computer science , biological system , cell , biology , computer vision , optics , microbiology and biotechnology , image (mathematics) , fluorescence , physics , genetics , cytoplasm , gene
Quantification of protein abundance and subcellular localization dynamics from fluorescence microscopy images is of high contemporary interest in cell and molecular biology. For large-scale studies of cell populations and for time-lapse studies, such quantitative analysis can not be performed effectively without some kind of automated image analysis tool. Here, we present fast algorithms for automatic cell contour recognition in bright field images, optimized to the model organism budding yeast (Saccharomyces cerevisiae). The cell contours can be used to effectively quantify cell morphology parameters as well as protein abundance and subcellular localization from overlaid fluorescence data.