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Correlation-based Method for Automatic Mitotic Cell Detection in Phase Contrast Microscopy

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Computer Recognition Systems

Part of the book series: Advances in Soft Computing ((AINSC,volume 30))

Abstract

A simple and fast method is presented which detects mitotic cells from two cell lines imaged in two phase-contrast microscopy techniques. Such detection is a first step to more sophisticated image processing tasks like determination of mitotic index or mitotic cell tracking in time-lapse movies. Detection algorithm is based on template matching approach that provides a list of candidates. The list is then pruned by validation algorithm that takes into account a priori information about mitotic cells. The method has been implemented as plugin for ImageJ and has been tested for several different data sets.

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References

  1. Bennett A.ÊH. (1951), Phase Microscopy: Principles and applications, Wiley, New York

    Google Scholar 

  2. Bradbury S., Evennett P. (1996), Phase Contrast and Modulation Contrast. In: Contrast Techniques in Light Microscopy, BIOS Scientific Publishers, Ltd., Oxford

    Google Scholar 

  3. Chanwimaluang T., Fan G. (2003), An effcient blood vessel detection algorithm for retinal images using entropy thresholding. IEEE International Symposium on Circuits and Systems

    Google Scholar 

  4. Frank J., Radermacher M., at el. (1996), Spider and web: Processing and visualization of images in 3d electron microscopy and related fields. Journal of Structural Biology, 116:190–199

    Article  Google Scholar 

  5. Glab G., Florczak K., Jaronski J., Licznerski T. (2001), Cyto-gynaecological diagnoses in phase contrast microscopy (in polish). Blackhorse Publishing

    Google Scholar 

  6. Kittler R., Buchholz F. (2003), RNA interference: gene silencing in the fast line. Seminars in Cancer Biology, 13:259–265

    Article  Google Scholar 

  7. Kivioja T., Ravantti J., Verkhovski A., Ukkonen E., Bamford D. (2000), Local average intenstiy-based method for identifying spherical particles in electron micrographs Journal of Structural Biology, 131:126–134

    Article  Google Scholar 

  8. Lewis P. (1995), Fast normalized cross-correlation. Vision Interface

    Google Scholar 

  9. Ludtke J., Baldwin P., Chiu W. (1999), EMAN: Semiautomated software for high-resolution single-particle reconstruction. Journal of Structural Biology, 128:82–97

    Article  Google Scholar 

  10. Boier Marti I.M., Martinescu D. C., Lynch R.E., Baker T.S. (1997–2005), Identification of spherical virus particles in digitized images of entire electron micrographs. Journal of Structural Biology, 120:146–157

    Article  Google Scholar 

  11. Miniello G. (1998), Colposcopy and Phase Contrast Microscopy. CIC Edizioni Internationale

    Google Scholar 

  12. Miniello G. (2001), Vaginal Fungal Infections by Phase Contrast Microscopy. CIC Edizioni Internationale

    Google Scholar 

  13. Nicholson W.V., Glaeser R.M. (2001), Review: Automatic particle detection in electron microscopy. Journal of Structural Biology, 133:90–101

    Article  Google Scholar 

  14. Nicholson W., Malladi R. (2004), Correlation-based methods of automatic particle detection in electron microscopy images with smotothing by anisotropic diffusion. Journal of Microscopy, 213:119–128

    Article  MathSciNet  Google Scholar 

  15. Press W.H., Teukolsky S.A., Vetterling W.T., Flannery B.P. (2004), Numerical Recipes in C. Press Syndicate of the University of Cambridge

    Google Scholar 

  16. Rasband W. S. (1997–2005), ImageJ

    Google Scholar 

  17. Sanderson J. (2002), Phase contrast microscopy. Encyclopedia of Life Sciences

    Google Scholar 

  18. Zernike F. (1935), Phase contrast method in microscopic observations (in german). Z. tech. Phys., 16:454

    Google Scholar 

  19. Zernike F. (1942), Phase contrast, a new method for microscopic observation of transparent objects. Physica, 9:686

    Article  Google Scholar 

  20. Zernike F. (1955), How I discovered phase contrast. Science, 121(3141):345–359

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Engineering Cybernetics, Wroclaw University of Technology, Wroclaw

    Lukasz Miroslaw & Artur Chorazyczewski

  2. Max Planck Institute of Molecular Cell Biology and Genetics Dresden, Dresden

    Lukasz Miroslaw, Frank Buchholz & Ralf Kittler

Authors
  1. Lukasz Miroslaw
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  2. Artur Chorazyczewski
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  3. Frank Buchholz
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  4. Ralf Kittler
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Editor information

Editors and Affiliations

  1. Faculty of Electronics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Marek Kurzyński , Edward Puchała , Michał Woźniak  & Andrzej żołnierek , ,  & 

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© 2005 Springer-Verlag Berlin Heidelberg

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Miroslaw, L., Chorazyczewski, A., Buchholz, F., Kittler, R. (2005). Correlation-based Method for Automatic Mitotic Cell Detection in Phase Contrast Microscopy. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_74

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  • DOI: https://doi.org/10.1007/3-540-32390-2_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25054-8

  • Online ISBN: 978-3-540-32390-7

  • eBook Packages: EngineeringEngineering (R0)

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