Image analysis algorithms for cell contour recognition in budding yeast.

Full Abstract

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.

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Author information

Author/s: Kvarnström, Mats (M); Logg, Katarina (K); Diez, Alfredo (A); Bodvard, Kristofer (K); Käll, Mikael (M);

Affiliation: Fraunhofer-Chalmers Research Centre, Chalmers Science Park, SE-412 88 Göteborg, Sweden. mats.kvarnstrom(-atsign-)fcc.chalmers.se

Journal and publication information

Publication Type: Evaluation Studies; Journal Article

Journal: Optics express (Opt Express), published in United States. (Language: eng)

Reference: 2008-Aug; vol 16 (issue 17) : pp 12943-57

Dates: Created 2008/08/19; Completed 2008/10/06;

PMID: 18711533, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artificial Intelligence
Cell Size
Image Interpretation, Computer-Assisted - methods

Image Interpretation, Computer-Assisted - methods
Microscopy - methods
Pattern Recognition, Automated - methods
Saccharomyces cerevisiae - cytology

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