Skip to main content
SpringerLink
Log in

Algorithm for Automatic Segmentation of Nuclear Boundaries in Cancer Cells in Three-Channel Luminescent Images

  • Published:
Journal of Applied Spectroscopy Aims and scope

We have developed an algorithm for segmentation of cancer cell nuclei in three-channel luminescent images of microbiological specimens. The algorithm is based on using a correlation between fluorescence signals in the detection channels for object segmentation, which permits complete automation of the data analysis procedure. We have carried out a comparative analysis of the proposed method and conventional algorithms implemented in the CellProfiler and ImageJ software packages. Our algorithm has an object localization uncertainty which is 2–3 times smaller than for the conventional algorithms, with comparable segmentation accuracy.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. O. Ronneberger, D. Baddeley, F. Scheipl, P. J. Verveer, H. Burkhardt, C. Cremer, L. Fahrmeir, T. Cremer, and B. Joffe, Chromosome Res., 16, No. 3, 523–562 (2008).

    Article  Google Scholar 

  2. H. Al-Ali, M. Blackmore, J. L. Bixby, and V. P. Lemmon, Assay Guidance Manual, Bethesda MD (2004).

  3. S. V. Ablameiko, V. V. Anishchenko, V. A. Lapitskii, and A. V. Tuzikov, Medical Information Technologies and Systems [in Russian], OIPI NAN Belarusi, Minsk (2007).

  4. G. G. Chung, M. P. Zerkowski, S. Ghosh, R. L. Camp, and D. L. Rimm, Lab. Invest., 87, No. 7, 662–669 (2007).

    Article  Google Scholar 

  5. R. L. Camp, G. G. Chung, and D. L. Rimm, Nature Medicine, 8, No. 11, 1323–1327 (2002).

    Article  Google Scholar 

  6. A. Niederlein, F. Meyenhofer, D. White, and M. Bickle, Comb. Chem. High Throughput Screen, 12, No. 9, 899–907 (2009).

    Article  Google Scholar 

  7. C. L. Adams and M. D. Sjaastad, Comb. Chem. High Throughput Screen, 12, No. 9, 877–887 (2009).

    Article  Google Scholar 

  8. V. Wiesmann, D. Franz, C. Held, C. Münzenmayer, R. Palmisano, and T. Wittenberg, J. Microsc., 257, No. 1, 39–53 (2015).

    Article  Google Scholar 

  9. G. K. Rohde, Conf. Proc. IEEE Eng. Med. Biol. Soc., 121–124 (2013).

  10. A. V. Tuzikov, S. A. Sheinin, and D. V. Zhuk, Mathematical Morphology, Moments, and Stereo Processing: Selected Topics in Digital Image Processing and Analysis [in Russian], Bel. Nauka, Minsk (2006).

  11. C. Sommer and D. W. Gerlich, J. Cell. Sci., 126, No. 24, 5529–5539 (2013).

    Article  Google Scholar 

  12. R. C. Gonzalez and R. E. Woods, Digital Image Processing [Russian translation], Tekhnosfera, Moscow (2006).

  13. S. V. Ablameiko and D. M. Lagunovskii, Image Processing: Technology, Methods, Application [in Russian], Amalfeya, Minsk (2000).

  14. J. Ghaye, M. A. Kamat, L. Corbino-Giunta, P. Silacci, G. Vergères, G. De Micheli, and S. Carrara , Cytometry A, 83, No. 11, 1001–1016 (2013).

  15. S. Ram, J. J. Rodriguez, and G. Bosco, Cytometry A, 81, No. 3, 198–212 (2012).

    Article  Google Scholar 

  16. A. Korzynska, L. Roszkowiak, C. Lopez, R. Bosch, L. Witkowski, and M. Lejeune, Diagn. Pathol., No. 8, 48 (2013).

  17. J. B. T. M. Roerdink and Ar. Meijster, Fund. Inform., 41, 187–228 (2001).

  18. D. Zhu, S. Jarmin, A. Ribeiro, F. Prin, S. Q. Xie, K. Sullivan, J. Briscoe, A. P. Gould, F. M. Marelli-Berg, and Y. Gu, Methods Mol. Biol., 616, 207–228 (2010).

    Article  Google Scholar 

  19. N. Malpica, C. O. de Solórzano, J. J. Vaquero, A. Santos, I. Vallcorba, J. M. García-Sagredo, and F. del Pozo, Cytometry, 28, 289–297 (1997).

    Article  Google Scholar 

  20. J. Cheng and J. C. Rajapakse, IEEE Trans. Biomed. Eng., 56, 741–748 (2009).

    Article  Google Scholar 

  21. M. Stöter, A. Niederlein, R. Barsacchi, F. Meyenhofer, H. Brandl, and M. Bickle, Methods Mol. Biol., 986, 105–122 (2013).

    Article  Google Scholar 

  22. C. A. Schneider, W. S. Rasband, and K. W. Eliceiri, Nat. Methods, 9, 671–675 (2012).

    Article  Google Scholar 

  23. S. Di Cataldo, E. Ficarra, A. Acquaviva, and E. Macii, Comput. Med. Imaging Graph., No. 34, 453–461 (2010).

  24. A. J. Berger, R. L. Camp, K. A. Divito, H. M. Kluger, R. Halaban, and D. L. Rimm, Cancer Res., No. 64, 8767–8772 (2004).

  25. Y. V. Lisitsa, M. M. Yatskou, V. V. Apanasovich, T. V. Apanasovich, and M. M. Shytsik, Zh. Prikl. Spektrosk., 81, No. 6, 907–913 (2014). [Y. V. Lisitsa, M. M. Yatskou, V. V. Apanasovich, T. V. Apanasovich, and M. M. Shytsik, J. Appl. Spectrosc., 81, 996–1003 (2014) (English translation)].

  26. G. Sharma, Digital Color Imaging Handbook, CRC Press (2002).

  27. S. A. Aivazyan, Ed., Applied Statistics: Classification and Dimensionality Reduction, Revised Edition [in Russian], Finansy i Statistika, Moscow (1989).

  28. P. K. Korshunova, in: Information Technologies and Systems 2014 [in Russian], BGUIR, Minsk (2014), pp. 266–267.

  29. N. Otsu, IEEE Transact. Systems, Man, and Cybernetics, 9, No. 1, 62–66 (1979).

    Article  MathSciNet  Google Scholar 

  30. Y. U. Lisitsa, M. M. Yatskou, V. V. Apanasovich, H. Rui, and T. V. Apanasovich, in: Proc. Eleventh Int. Conf. on Pattern Recognition and Information Processing (PRIP 2011), 18–20 May 2011, BSUIR, Minsk (2011), pp. 116–120.

  31. T. F. Coleman and Y. Li, SIAM J. Optimization, No. 6, 418–445 (1996).

  32. S. Di Cataldo, E. Ficarra, A. Acquaviva, and E. Macii, Comput. Methods Programs Biomed., 100, No. 1, 1–15 (2010).

    Article  Google Scholar 

  33. Y. Guo, X. Xu, Y. Wang, Y. Wang, S. Xia, and Z. Yang, Microsc. Res. Tech., 77, No. 8, 547–559 (2014).

    Article  Google Scholar 

  34. L. Kamentsky, T. R. Jones, A. Fraser, M. A. Bray, D. J. Logan, K. L. Madden, V. Ljosa, C. Rueden, K. W. Eliceiri, and A. E. Carpenter, Bioinformatics (Oxford, England), 27, No. 8, 1179–1180 (2011).

  35. N. Milstein, Tech Rep., Technion, Israel Institute of Technology, 1–38 (1998).

Download references

Author information

Authors and Affiliations

  1. Belorussian State University, 4 Nezavisimost′ Ave., Minsk, 220030, Belarus

    Y. V. Lisitsa, M. M. Yatskou, V. V. Apanasovich & T. V. Apanasovich

Authors
  1. Y. V. Lisitsa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. M. Yatskou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. V. Apanasovich
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. T. V. Apanasovich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Y. V. Lisitsa.

Additional information

Translated from Zhurnal Prikladnoi Spektroskopii, Vol. 82, No. 4, pp. 598–607, July–August, 2015.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lisitsa, Y.V., Yatskou, M.M., Apanasovich, V.V. et al. Algorithm for Automatic Segmentation of Nuclear Boundaries in Cancer Cells in Three-Channel Luminescent Images. J Appl Spectrosc 82, 634–643 (2015). https://doi.org/10.1007/s10812-015-0156-2

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10812-015-0156-2

Keywords

Search

Navigation