Skip to main content
SpringerLink
Log in

Selection and Quantification of Objects in Microscopic Images: from Multi-Criteria to Multi-Threshold Analysis

  • Published:
BioNanoScience Aims and scope Submit manuscript

Abstract

Due to the increased number of applications of both microscopic imaging and image analysis including biomedical studies, the design of specialized algorithms and tools to facilitate quantitative assessment of objects in the image content is of urgent need. Recently, a number of approaches ranging from object counting by machine learning methods to statistical image analysis have been suggested and successfully implemented to resolve the cell quantification problem. Here, we revisit the above problem considering samples where objects presented in the same images have to be explicitly distinguished and quantified without involving any dedicated experimental setting like differential fluorescent staining. We consider several possible classification criteria and show explicitly how their combination in a single algorithm can be used to improve results in complex images where single criteria-based rules inevitably fail. Finally, we suggest a possible approach for the analysis of non-homogeneous images based on combining object selection results for different threshold values thus enhancing the algorithm from multi-criteria to multi-threshold analysis. To demonstrate the performance of the suggested solutions, we show several prominent examples of complex structures ranging from images containing both live and apoptotic cells as well as containing mixtures of globular and fibrous forms of heat-shock protein IbpA.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Atale, N., Gupta, S., Yadav, U. C. S., & Rani, V. (2014). Cell-death assessment by fluorescent and nonfluorescent cytosolic and nuclear staining techniques. Journal of Microscopy, 255(1), 7–19. https://doi.org/10.1111/jmi.12133.

    Article  Google Scholar 

  2. Netuschil, L., Auschill, T. M., Sculean, A., & Arweiler, N. B. (2014). Confusion over live/dead stainings for the detection of vital microorganisms in oral biofilms - which stain is suitable?. BMC Oral Health: 14. https://doi.org/10.1186/1472-6831-14-2

  3. van der Pol, E., Hoekstra, A. G., Sturk, A., Otto, C., van Leeuwen, T. G., & Nieuwland, R. (2010). Optical and non-optical methods for detection and characterization of microparticles and exosomes. Journal of Thrombosis and Haemostasis, 8(12), 2596–2607. https://doi.org/10.1111/j.1538-7836.2010.04074.x.

    Article  Google Scholar 

  4. Kayumov, A. R., Khakimullina, E. N., Sharafutdinov, I. S., Trizna, E. Y., Latypova, L. Z., Lien, H. T., Margulis, A. B., Bogachev, M. I., & Kurbangalieva, A. R. (2015). Inhibition of biofilm formation in Bacillus subtilis by new halogenated furanones. The Journal of Antibiotics, 68(5), 297–301. https://doi.org/10.1038/ja.2014.143.

    Article  Google Scholar 

  5. Kayumov, A. R., Nureeva, A. A., Trizna, E. Y., Gazizova, G. R., Bogachev, M. I., Shtyrlin, N. V., Pugachev, M. V., Sapozhnikov, S. V., & Shtyrlin, Y. G. (2015). New derivatives of pyridoxine exhibit high antibacterial activity against biofilm-embedded staphylococcus cells. BioMed Research International, 2015. https://doi.org/10.1155/2015/890968.

  6. Trizna, E., Latypova, L., Kurbangalieva, A., Bogachev, M. I., & Kayumov, A. (2016). 2(5H)-Furanone derivatives as inhibitors of staphylococcal biofilms. BioNanoScience, 6(4), 423–426.

    Article  Google Scholar 

  7. Baidamshina, D. R., Trizna, E. Y., Holyavka, M. G., Bogachev, M. I., Artyukhov, V. G., Akhatova, F. S., et al. (2017). Targeting microbial biofilms using Ficin, a nonspecific plant protease. Scientific Reports, 7. https://doi.org/10.1038/srep46068.

  8. Sharafutdinov, I., Pavlova, A., Khabibrakhmanova, A., Kurbangalieva, A., & Kayumov, A. (2017). The antimicrobial effect of the 5-((-)bornyloxy)-2(5H)-furanone derivative on gram-positive bacteria. The FEBS Journal, 284, 143.

    Google Scholar 

  9. Trizna, E. Y., Khakimullina, E. N., Latypova, L. Z., Kurbangalieva, A. R., Sharafutdinov, I. S., Evtyugin, V. G., Babynin, E. V., Bogachev, M. I., & Kayumov, A. R. (2015). Thio derivatives of 2(5H)-Furanone as inhibitors against Bacillus subtilis biofilms. Acta Naturae, 7(2), 102–107.

    Google Scholar 

  10. Volkov, V. (2015). Extraction of extended small-scale objects in digital images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-5/W6, 87–93.

    Article  Google Scholar 

  11. Klinger-Strobel M, Suesse H, Fischer D, Pletz MW, Makarewicz O (2016) A novel computerized cell count algorithm for biofilm analysis. PLoS One: 11(5). https://doi.org/10.1371/journal.pone.0154937

  12. Bogachev, M. I., Volkov, V. Y., Markelov, O. A., Trizna, E. Y., Baydamshina, D. R., Melnikov, V., Murtazina, R. R., Zelenikhin, P. V., Sharafutdinov, I. S., Kayumov, A. R. (2018). Fast and simple tool for the quantification of biofilm-embedded cells sub-populations from fluorescent microscopic images. PLoS One, 13(5). https://doi.org/10.1371/journal.pone.0193267

  13. Heydorn, A., Nielsen, A. T., Hentzer, M., Sternberg, C., Givskov, M., Ersboll, B. K., & Molin, S. (2000). Quantification of biofilm structures by the novel computer program COMSTAT. Microbiology-Sgm, 146, 2395–2407. https://doi.org/10.1099/00221287-146-10-2395.

    Article  Google Scholar 

  14. Beyenal, H., Donovan, C., Lewandowski, Z., & Harkin, G. (2004). Three-dimensional biofilm structure quantification. Journal of Microbiology Methods, 59(3), 395–413. https://doi.org/10.1016/j.mimet.2004.08.003.

    Article  Google Scholar 

  15. Merritt, J. H., Kadouri, D. E., O’Toole, G. A. (2005). Growing and analyzing static biofilms. Current Protocols in Microbiology: Chapter 1:Unit 1B.1. https://doi.org/10.1002/9780471729259.mc01b01s00

  16. Ji, L., Piper, J., & Tang, J. Y. (1989). Erosion and dilation of binary images by arbitrary structuring elements using interval coding. Pattern Recognition Letters, 9(3), 201–209.

    Article  MATH  Google Scholar 

  17. Adams, R., & Bischof, L. (1994). Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), 641–647. https://doi.org/10.1109/34.295913.

    Article  Google Scholar 

  18. Nattkemper, T. W., Twellmann, T., Ritter, H., & Schubert, W. (2003). Human vs. machine: Evaluation of fluorescence micrographs. Computers in Biology and Medicine, 33(1), 31–43.

    Article  Google Scholar 

  19. Tchoukalova, Y. D., Harteneck, D. A., Karwoski, R. A., Tarara, J., & Jensen, M. D. (2003). A quick, reliable, and automated method for fat cell sizing. Journal of Lipid Research, 44(9), 1795–1801. https://doi.org/10.1194/jlr.D300001-JLR200.

    Article  Google Scholar 

  20. Daims, H., Lucker, S., & Wagner, M. (2006). daime, a novel image analysis program for microbial ecology and biofilm research. Environmental Microbiology, 8(2), 200–213. https://doi.org/10.1111/j.1462-2920.2005.00880.x.

    Article  Google Scholar 

  21. Vishnyakov, I. E., Bogachev, M. I., Salafutdinov, I., Borchsenius, S. N., & Kayumov, A. R. (2016). The temperature-dependent selectivity of potential interaction partners for the small heat shock protein IbpA from Acholeplasma laidlawii. BioNanoScience, 6(4), 437–442.

    Article  Google Scholar 

  22. Kayumov, A. R., Bogachev, M. I., Manuvera, V. A., Lazarev, V. N., Sabantsev, A. V., Artamonova TO, Borchsenius, S. N., & Vishnyakov, I. E. (2017). Recombinant small heat shock protein from Acholeplasma laidlawii increases the Escherichia coli viability in thermal stress by selective protein rescue. Molecular Biology, 51(1), 112–121. https://doi.org/10.7868/S0026898417010086.

    Article  Google Scholar 

Download references

Funding

The conceptualization of the image analysis methodology and algorithm design has been performed in the framework of the basic state assignment by the Ministry of Science and Higher Education of the Russian Federation to St. Petersburg Electrotechnical University (project No. 2.5475.2017/6.7 to Mikhail I Bogachev). Preparation of experimental biological samples and their microscopic imaging have been supported by the Russian Science Foundation (project No. 17-74-20065 to Innokentii Vishnyakov).

Author information

Authors and Affiliations

  1. Radio Systems Department & Biomedical Engineering Research Centre, St. Petersburg Electrotechnical University, 5 Professor Popov street, St. Petersburg, Russia, 197376

    Mikhail I. Bogachev, Vladimir Yu Volkov & Gleb Kolaev

  2. Molecular Genetics of Microorganisms Lab, Kazan Federal University, Institute of Fundamental Medicine and Biology, 18 Kremlyovskaya street, Kazan, Russia, 420008

    Liliya Chernova & Airat Kayumov

  3. Institute of Cytology of the Russian Academy of Science, 4 Tikhoretsky avenue, St. Petersburg, Russia, 194064

    Liliya Chernova, Innokentii Vishnyakov & Airat Kayumov

Authors
  1. Mikhail I. Bogachev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vladimir Yu Volkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gleb Kolaev
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liliya Chernova
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Innokentii Vishnyakov
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Airat Kayumov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Airat Kayumov.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bogachev, M.I., Volkov, V.Y., Kolaev, G. et al. Selection and Quantification of Objects in Microscopic Images: from Multi-Criteria to Multi-Threshold Analysis. BioNanoSci. 9, 59–65 (2019). https://doi.org/10.1007/s12668-018-0588-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12668-018-0588-2

Keywords

Search

Navigation