Machine Vision and Applications

, Volume 23, Issue 4, pp 607–621 | Cite as

Automatic segmentation of adherent biological cell boundaries and nuclei from brightfield microscopy images

  • Rehan AliEmail author
  • Mark Gooding
  • Tünde Szilágyi
  • Borivoj Vojnovic
  • Martin Christlieb
  • Michael Brady
Special Issue Paper


The detection and segmentation of adherent eukaryotic cells from brightfield microscopy images represent challenging tasks in the image analysis field. This paper presents a free and open-source image analysis package which fully automates the tasks of cell detection, cell boundary segmentation, and nucleus segmentation in brightfield images. The package also performs image registration between brightfield and fluorescence images. The algorithms were evaluated on a variety of biological cell lines and compared against manual and fluorescence-based ground truths. When tested on HT1080 and HeLa cells, the cell detection step was able to correctly identify over 80% of cells, whilst the cell boundary segmentation step was able to segment over 75% of the cell body pixels, and the nucleus segmentation step was able to correctly identify nuclei in over 75% of the cells. The algorithms for cell detection and nucleus segmentation are novel to the field, whilst the cell boundary segmentation algorithm is contrast-invariant, which makes it more robust on these low-contrast images. Together, this suite of algorithms permit brightfield microscopy image processing without the need for additional fluorescence images. Finally our sephaCe application, which is available at, provides a novel method for integrating these methods with any motorised microscope, thus facilitating the adoption of these techniques in biological research labs.


Segmentation Registration Cell detection Level sets Monogenic signal Continuous intrinsic dimensionality 


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  1. 1.
    Ali, R.: Applications of microscopy image analysis and modelling in characterising the mechanisms of hypoxia-mediated drug resistance. Ph.D thesis, University of Oxford, Oxford (2009)Google Scholar
  2. 2.
    Ali, R., Gooding, M., Christlieb, M., Brady, M.: Phase-based segmentation of cells from brightfield microscopy. In: Proceedings of the International Symposium Biomedical Imaging (ISBI), pp 57–60 (2007)Google Scholar
  3. 3.
    Ali, R., Gooding, M., Christlieb, M., Brady, M.: Advanced phase-based segmentation of multiple cells from brightfield microscopy images. In: Proceedings of the International Symposium Biomedical Imaging (ISBI), pp 181–184 (2008)Google Scholar
  4. 4.
    Ali, R., Szilagyi, T., Gooding, M., Christlieb, M., Brady, M.: On the use of lowpass filters for image processing with inverse laplacian models. J. Math. Imag. Vis. Under Rev. (2010)Google Scholar
  5. 5.
    Barber, P.R., Locke, R.J., Pierce, G.P., Rothkamm, K., Vojnovic, B.: Gamma-H2AX focicounting: image processing and control software for high-content screening. In: Proceedings of SPIE, San Jose, CA,USA, pp 64, 411M–64, 411M–10 (2007)Google Scholar
  6. 6.
    Boukerroui M., Noble D., Brady A.: On the choice of band-pass quadraturefilters. J. Math. Imag. Vis. 21, 53–80 (2004)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Bradbury, L.: Segmentation of bright-field cell images. Ph.D thesis, University of Waterloo, Ontario, Canada (2009)Google Scholar
  8. 8.
    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
  9. 9.
    Cates J., Lefohn A., Whitaker R.: GIST: an interactive, GPU-Based level set segmentation tool for 3D medical images. Med. Imag. Anal. 8, 217–231 (2004)CrossRefGoogle Scholar
  10. 10.
    Curl C., Harris T., Harris P., Allman B., Bellair C., Stewart A., Delbridge L.: Quantitative phase microscopy: a new tool for measurement of cell culture growth and confluency in situ. Eur. J. Physiol. 448, 462–468 (2004)CrossRefGoogle Scholar
  11. 11.
    Dice, L. (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302Google Scholar
  12. 12.
    Felsberg M., Sommer G.: The monogenic signal. IEEE Trans. Sig. Proc. 49(12), 3136–3144 (2001)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Felsberg M., Kalkan S., Krüger N.: Continuous dimensionality characterization of image structures. Imag. Vis. Comput. 27(6), 628–636 (2009)CrossRefGoogle Scholar
  14. 14.
    Folkard M., Prise K.M., Grime G., Kirkby K., Vojnovic B.: The use of microbeamsto investigate radiation damage in living cells. Appl. Rad. Isot. 67(3), 436–439 (2009)CrossRefGoogle Scholar
  15. 15.
    Gooding M., Kennedy S., Noble J.: Volume segmentation and reconstruction from freehand 3D ultrasound data with application to ovarian follicle measurement. Ultrasound Med. Biol. 34(2), 183–195 (2008)CrossRefGoogle Scholar
  16. 16.
    Gordon A., Colman-Lerner A., Chin T., Benjamin K., Yu R., Brent R.: Single-cell quantification of molecules and rates using open-source microscope-based cytometry. Nat. Methods 4, 175–181 (2007)CrossRefGoogle Scholar
  17. 17.
    Korzynska A., Stronjy W., Hoppe A., Wertheim D., Hoser P.: Segmentation of microscope images of living cells. Pattern Anal. Appl. 10(4), 301–319 (2007)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Mellor M., Brady M.: Phase mutual information as a similarity measure for registration. Med. Image Anal. 9(4), 330–343 (2005)CrossRefGoogle Scholar
  19. 19.
    Nilsson B., Heyden A.: A fast algorithm for level set-like active contours. Pattern Rec. Lett. 24, 1331–1337 (2003)zbMATHCrossRefGoogle Scholar
  20. 20.
    Otsu N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)CrossRefGoogle Scholar
  21. 21.
    Paganin D., Nugent K.: Noninterferometric phase imaging with partially coherent light. Phys. Rev. Lett. 80(12), 2586–2589 (1998)CrossRefGoogle Scholar
  22. 22.
    Paganin D., Barty A., McMahon P., Nugent K.: Quantitative phase-amplitude microscopy. III. The effects of noise. J. Microsc. 214, 51–61 (2004)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Rittscher J., Machiraju R., Wong S.T.C.: Microscopic Image Analysis for Life Science Applications, 1st edn. Artech House Publishers, USA (2008)Google Scholar
  24. 24.
    Rote G.: Computing the minimum Hausdorff distance between two point sets on a line under translation. Inf. Process. Lett. 38, 123–127 (1991)MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Selinummi, J., Ruusuvuori, P., Podolsky, I., Ozinsky, A., Gold, E., Yli-Harja, O., Aderem, A., Shmulevich, I.: Bright field microscopy as an alternative to whole cell fluorescence in automated analysis of macrophage images 4(10):e7497 (2009). doi: 10.1371/journal.pone.0007497
  26. 26.
    Styner M., Brehbuhler C., Szekely G., Gerig G.: Parametric estimate of intensity homogeneities applied to MRI. IEEE Trans. Med. Imag. 19(3), 153–165 (2000)CrossRefGoogle Scholar
  27. 27.
    Teague M.: Deterministic phase retrieval: a green’s function solution. J. Opt. Soc. Am. 73, 1434–1441 (1983)CrossRefGoogle Scholar
  28. 28.
    Tscherepanow M., Zollner F., Kummert F.: Classification of segmented regions in brightfield microscope images. ICPR 06(3), 972–975 (2006)Google Scholar
  29. 29.
    Tscherepanow M., Nickels N., Kummert F.: Recognition of unstained live drosophila cells in microscope image, pp. 169–176. IMVIP, Los Alamitos (2007)Google Scholar
  30. 30.
    Veselov O., Polak W., Ugenskiene R., Lebed K., Lekki J., Stachura Z., Styczen J.: Development of the IFJ single ion hit facility for cell irradiation. Radiat. Prot. Dosim. 122, 316–319 (2006)CrossRefGoogle Scholar
  31. 31.
    Volkov V., Zhu Y., Graef M.D.: A new symmetrized solution for phase retrieval using the transport of intensity equation. Micron. 33(5), 411–416 (2002)CrossRefGoogle Scholar
  32. 32.
    Wang K., Chang A., Kale L., Dantzig A.: Parallelization of a level set method for simulating dendritic growth. J. Parallel Distrib. Comput. 11, 1379–1386 (2006)CrossRefGoogle Scholar
  33. 33.
    Wu K., Gauthier D., Levine M.: Live cell image segmentation. IEEE Trans. Biomed. Eng. 42(1), 1–12 (1995)CrossRefGoogle Scholar
  34. 34.
    Xu C., Prince J.: Snakes, shapes and gradient vector flow. IEEE Trans. Image. Proc. 7, 359–369 (1998)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Rehan Ali
    • 1
    Email author
  • Mark Gooding
    • 2
  • Tünde Szilágyi
    • 3
  • Borivoj Vojnovic
    • 4
  • Martin Christlieb
    • 4
  • Michael Brady
    • 3
  1. 1.Department of Radiation PhysicsStanford UniversityStanfordUSA
  2. 2.Mirada Medical LtdOxfordUK
  3. 3.Department of Engineering Science, FRS FREng FMedSci Wolfson Medical Vision LabUniversity of OxfordOxfordUK
  4. 4.Gray Institute for Radiation Oncology and BiologyUniversity of OxfordOxfordUK

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