Skip to main content

Segmentation of Moving Cells in Bright Field and Epi-Fluorescent Microscopic Image Sequences

  • Conference paper
Computer Vision and Graphics (ICCVG 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6374))

Included in the following conference series:

Abstract

The monitoring of the dynamics of stem cells’ growth in culture is important in regenerative medicine. In this paper the method of cells’ images segmentation based on alternating microscopic imaging with bright field (BF) and epifluorescent (EF) images is proposed. The method consists of two principal stages: coarse segmentation of EF images followed by fine segmentation on BF ones. The latter is based on the morphological watershed from markers produced in the first stage. Due to the fact that sequence of EF is shorter than BF one, markers cannot be produced directly for all BF images. In order to create them, an additional step of morphological interpolation of markers is applied.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Iwanowski, M., Korzyńska, A.: Detection of the area covered by neural stem cells in cultures using textural segmentation and morphological watershed. In: ASC; Computer Recognition Systems, vol. 3, pp. 543–557. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  2. Tse, S., Bradbury, L., Wan, J.W.L., Djambazian, H., Sladek, R., Hudson, T.: A combined watershed and level set method for segmentation of brightfield cell images. In: Proceedings of the SPIE, vol. 7258, pp. 72593G–72593G-10 (2009)

    Google Scholar 

  3. Iwanowski, M.: Metody morfologiczne w przetwarzaniu obraz”ow cyfrowych, AOW EXIT (2009)

    Google Scholar 

  4. Korzyńska, A., Iwanowski, M., Neuman, U., Dobrowolska, E., Hoser, P.: Comparison of the methods of microscopic image segmentation. In: Dossel, S. (ed.) WC 2009, IFMBE Proceedings, vol. 25/IV, pp. 425–428 (2009) ISBN 978-3-642-03897-6 (book), ISSN 1680-0737 (CD)

    Google Scholar 

  5. 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. PLoS ONE 4(10), e7497 (2009)

    Google Scholar 

  6. Ramoser, H.: Leukocyte segmentation and SVM classification in blood smear images. Machine Graphics and Vision 17(1/2), 187–200 (2008)

    Google Scholar 

  7. Piętka, B.D., Dulewicz, A., Jaszczak P.: Removing artefacts from microscopic Images of cytological smears. Machine Graphics and Vision 17(1/2),131–152 (2008)

    Google Scholar 

  8. Korzyńska, A., Zdunczuk, M.: Clustering as a method of image simplification. In: Pietka, K. (ed.) Inform. Tech. in Biomed., ASC, vol. 47, pp. 365–376 (2008)

    Google Scholar 

  9. Marciniak, A., Nieczkowski, T., Obuchowicz, A.: Color Homogram for segmentation of fine needle biopsy images. Machine Graphics and Vision 17(1/2), 153–165 (2008)

    Google Scholar 

  10. Witkowski, L.: A computer system for cells motility Evaluation. Machine Graphics and Vision 17(1/2), 167–186 (2008)

    Google Scholar 

  11. Markiewcz, T., Osowski, S.: Morphological operations for blood cells extraction from the image of the bone marrow smear. Przeglad Elektrotechniczny 84(5), 24–26 (2008)

    Google Scholar 

  12. Korzyńska, A., Iwanowski, M.: Detection of mitotic cell fraction in stem cells in cultures. In: ASC; Information Technologies in Biomedicine 1, vol. 47, pp. 365–376. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  13. Korzyńska, A., Strojny, W., Hoppe, A., Wertheim, D., Hoser, P.: Segmentation of microscope images of living cells. Pattern Anal Applic. 10, 301–319 (2007)

    Article  Google Scholar 

  14. Buzanska, L., Jurga, M., Stachowiak, E.K., Stachowiak, M.K., Domanska-Janik, K.: Neural Stem-like Cell Line Derived from a Nonhematopoietic Population of Human Umbilical Cord Blood. Stem Cell and Development 15, 391–406 (2006)

    Article  Google Scholar 

  15. Boier Marti, I.M., Martineus, D.C.: Identification of spherical virus particles in digitized images of entire electron micrographs. Journal of Structural Biology 120, 146–157 (2005)

    Article  Google Scholar 

  16. Koprowski, R., Wr”oblewski, Z.: Automatic segmentation of biological cell structures based on conditional opening and closing. Machine Graphics and Vision 14, 285–307 (2005)

    Google Scholar 

  17. Sabino, D.M.D., da F Costa, F., Costa, L., Rizzatti, E.G., Zago, M.A.: A texture approach to leukocyte recognition. Real-Time Imaging 10(4), 205–216 (2004)

    Article  Google Scholar 

  18. Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Heidelberg (2004)

    Google Scholar 

  19. Jiang, K., Liao, Q.M., Dai, S.Y.: A novel white blood cell segmentation scheme using scale-space altering and watershed clustering. In: Proc. Int. Conf. on Machine Learning and Cybernetics, vol. 5, pp. 2820–2825 (2003)

    Google Scholar 

  20. Buzanska, L., Machaj, E.K., Zablocka, B.: Human Cord Blood - Derived Cells Attain Neuronal and Glial Features in Vitro. J. Cell Sci. 115, 2131–2138 (2002)

    Google Scholar 

  21. Periasamy, A.: Methods in Cellular Imaging. Oxford University Press, Oxford (2001)

    Google Scholar 

  22. Comaniciu, D., Meer, P.: Cell image segmentation for diagnostic pathology. In: Advanced Algorithmic Approaches to Medical Image Segmentation: State-of-the-Art Application in Cardiology, Neurology, Mammography and Pathology, pp. 541–558 (2001)

    Google Scholar 

  23. Iwanowski, M., Serra, J.: The morphological-affine object deformation. In: Mathematical Morphology and its Applications to Signal and Image Processing, pp. 81–90. Kluwer Academic Publishers, Dordrecht (2000)

    Google Scholar 

  24. Pham, D.L., Xu, C., Prince, J.L.: A survey of current methods in medical image segmentation, Vol. 2, pp. 315–338 (2000)

    Google Scholar 

  25. Serra, J.: Hausdorff distance and interpolations. In: Mathematical morphology and its Applications to Image and Signal Processing, pp. 107–114. Kluwer Academic Publishers, Dordrecht (1998)

    Google Scholar 

  26. Meyer, F.: A morphological interpolation method for mosaic images. In: Mathematical Morphology and its Applications to Image and Signal Processing, pp. 337–344. Kluwer Academic Publishers, Dordrecht (1996)

    Google Scholar 

  27. Serra, J.: Image analysis and mathematical morphology, vol. 1. Academic Press, London (1983)

    Google Scholar 

  28. Fu, K.S., Mui, J.K.: A survey on image segmentation. Pattern Recognition 13(1), 3–16 (1981)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Iwanowski, M., Korzyńska, A. (2010). Segmentation of Moving Cells in Bright Field and Epi-Fluorescent Microscopic Image Sequences. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2010. Lecture Notes in Computer Science, vol 6374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15910-7_46

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15910-7_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15909-1

  • Online ISBN: 978-3-642-15910-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics