Computing and Visualization in Science

, Volume 14, Issue 7, pp 341–352 | Cite as

An active-contour based algorithm for the automated segmentation of dense yeast populations on transmission microscopy images

  • Kristian BrediesEmail author
  • Heimo Wolinski
Original Article


An image-processing pipeline for the automated segmentation of yeast cells in microscopy images is proposed. The method is suitable for the non-invasive detection of individual cells in transmission data which can be acquired simultaneously with fluorescence data. It moreover takes the varying quality and highly heterogeneous characteristics of cells in transmission images into account, is capable to process images with dense yeast populations and can be used to extract quantitative cell-based data from transmission/fluorescence image pairs. Applicability and performance of the method is evaluated on a data set of 523 different yeast deletion mutant strains.


Cell segmentation Active contour algorithm Transmission microscopy Dense yeast populations 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bredies, K.: A variational weak weighted derivative: Sobolev spaces and degenerate elliptic equations. Tech. rep., University of Bremen (2008)Google Scholar
  2. 2.
    Buades A., Coll B., Morel J.M.: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4(2), 490–530 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Carpenter A.E., Jones T.R., Lamprecht M.R., Clarke C., Kang I.H., Friman O., Guertin D.A., Chang J.H., Lindquist R.A., Moffat J., Golland P., Sabatini D.M.: Cellprofiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7(10), R100 (2006)CrossRefGoogle Scholar
  4. 4.
    Caselles V., Catté F., Coll T., Dibos F.: A geometric model for active contours in image processing. Numerische Mathematik 66(1), 1–31 (1993)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Caselles V., Kimmel R., Sapiro G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)zbMATHCrossRefGoogle Scholar
  6. 6.
    Catté F., Lions P.L., Morel J.M., Coll T.: Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. Numer. Anal. 29(1), 182–193 (1992)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Chan T.F., Shen J.: Image Processing and Analysis—Variational, PDE, Wavelet, and Stochastic Methods. SIAM, Philadelphia (2005)zbMATHCrossRefGoogle Scholar
  8. 8.
    Hamilton N.A., Pantelic R.S., Hanson K., Teasdale R.D.: Fast automated cell phenotype image classification. BMC Bioinf. 8, 110 (2007)CrossRefGoogle Scholar
  9. 9.
    Huh S., Lee D., Murphy R.F.: Efficient framework for automated classification of subcellular patterns in budding yeast. Cytom. A 75(11), 934–940 (2009)CrossRefGoogle Scholar
  10. 10.
    Huh W.K., Falvo J.V., Gerke L.C., Carroll A.S., Howson R.W., Weissman J.S., O’Shea E.K.: Global analysis of protein localization in budding yeast. Nature 425(6959), 686–691 (2003)CrossRefGoogle Scholar
  11. 11.
    Kass M., Witkin A., Terzopoulos D.: Snakes: Active Contour Models. Int. J. Comput. Vis. 1(4), 321–331 (1988)CrossRefGoogle Scholar
  12. 12.
    Kvarnstrom M., Logg K., Diez A., Bodvard K., Kall M.: Image analysis algorithms for cell contour recognition in budding yeast. Opt. Express 16(17), 12943–12957 (2008)CrossRefGoogle Scholar
  13. 13.
    Li Z., Vizeacoumar F.J., Bahr S., Li J., Warringer J., Vizeacoumar F.S., Min R., Vandersluis B., Bellay J., Devit M., Fleming J.A., Stephens A., Haase J., Lin Z.Y., Baryshnikova A., Lu H., Yan Z., Jin K., Barker S., Datti A., Giaever G., Nislow C., Bulawa C., Myers C.L., Costanzo M., Gingras A.C., Zhang Z., Blomberg A., Bloom K., Andrews B., Boone C.: Systematic exploration of essential yeast gene function with temperature-sensitive mutants. Nat. Biotechnol. 29(4), 361–367 (2011)zbMATHCrossRefGoogle Scholar
  14. 14.
    Ohnuki S., Oka S., Nogami S., Ohya Y.: High-content, image-based screening for drug targets in yeast. PLoS One 5(4), e10177 (2010)CrossRefGoogle Scholar
  15. 15.
    Ohya Y., Sese J., Yukawa M., Sano F., Nakatani Y., Saito T.L., Saka A., Fukuda T., Ishihara S., Oka S., Suzuki G., Watanabe M., Hirata A., Ohtani M., Sawai H., Fraysse N., Latge J.P., Francois J.M., Aebi M., Tanaka S., Muramatsu S., Araki H., Sonoike K., Nogami S., Morishita S.: High-dimensional and large-scale phenotyping of yeast mutants. Proc. Natl. Acad. Sci. USA 102(52), 19015–19020 (2005)CrossRefGoogle Scholar
  16. 16.
    Perona P., Malik J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)CrossRefGoogle Scholar
  17. 17.
    Saito T.L., Ohtani M., Sawai H., Sano F., Saka A., Watanabe D., Yukawa M., Ohya Y., Morishita S.: Scmd: saccharomyces cerevisiae morphological database. Nucleic Acids Res. 32(Database issue), D319–D322 (2004)CrossRefGoogle Scholar
  18. 18.
    Saito T.L., Sese J., Nakatani Y., Sano F., Yukawa M., Ohya Y., Morishita S.: Data mining tools for the saccharomyces cerevisiae morphological database. Nucleic Acids Res. 33(Web Server issue), W753–W757 (2005)CrossRefGoogle Scholar
  19. 19.
    The MathWorks Inc.: MATLAB, version 7.11.0 (R2010b) (2010)Google Scholar
  20. 20.
    Visage Imaging GmbH: Amira 5.3 Microscopy/ResolveRT (2011)Google Scholar
  21. 21.
    Vizeacoumar F.J., van Dyk N., Vizeacoumar F.S., Cheung V., Li J., Sydorskyy Y., Case N., Li Z., Datti A., Nislow C., Raught B., Zhang Z., Frey B., Bloom K., Boone C., Andrews B.J.: Integrating high-throughput genetic interaction mapping and high-content screening to explore yeast spindle morphogenesis. J. Cell Biol. 188(1), 69–81 (2010)CrossRefGoogle Scholar
  22. 22.
    Weickert J.: Anisotropic Diffusion in Image Processing. Teubner, Stuttgart (1998)zbMATHGoogle Scholar
  23. 23.
    Wolinski H., Petrovic U., Mattiazzi M., Petschnigg J., Heise B., Natter K., Kohlwein S.D.: Imaging-based live cell yeast screen identifies novel factors involved in peroxisome assembly. J. Proteome Res. 8(1), 20–27 (2009)CrossRefGoogle Scholar
  24. 24.
    Xu C., Prince J.L.: Snakes, shapes and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Institute of Mathematics and Scientific ComputingUniversity of GrazGrazAustria
  2. 2.Institute of Molecular Biosciences, Department of BiochemistryUniversity of GrazGrazAustria

Personalised recommendations