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Development of an automated asbestos counting software based on fluorescence microscopy

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Abstract

An emerging alternative to the commonly used analytical methods for asbestos analysis is fluorescence microscopy (FM), which relies on highly specific asbestos-binding probes to distinguish asbestos from interfering non-asbestos fibers. However, all types of microscopic asbestos analysis require laborious examination of large number of fields of view and are prone to subjective errors and large variability between asbestos counts by different analysts and laboratories. A possible solution to these problems is automated counting of asbestos fibers by image analysis software, which would lower the cost and increase the reliability of asbestos testing. This study seeks to develop a fiber recognition and counting software for FM-based asbestos analysis. We discuss the main features of the developed software and the results of its testing. Software testing showed good correlation between automated and manual counts for the samples with medium and high fiber concentrations. At low fiber concentrations, the automated counts were less accurate, leading us to implement correction mode for automated counts. While the full automation of asbestos analysis would require further improvements in accuracy of fiber identification, the developed software could already assist professional asbestos analysts and record detailed fiber dimensions for the use in epidemiological research.

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Notes

  1. The automated counting software and the fluorescent probe can be provided for the purpose of testing. The software can be provided free of charge with the temporary license for the duration of testing.

References

  • Baron, P. A. (2001). Measurement of airborne fibers: A review. Industrial Health, 39, 39–50.

    Article  CAS  Google Scholar 

  • Cho, M. O., Chang, H. M., Lee, D., Yu, Y. G., Han, H., & Kim, J. K. (2013). Selective detection and automated counting of fluorescently-labeled chrysotile asbestos using a dual-mode high-throughput microscopy (DM-HTM) method. Sensors, 13, 5686–5699.

    Article  CAS  Google Scholar 

  • Davis, J. M., Beckett, S. T., Bolton, R. E., Collings, P., & Middleton, A. P. (1978). Mass and number of fibres in the pathogenesis of asbestos-related lung disease in rats. British Journal of Cancer, 37, 673–688.

    Article  CAS  Google Scholar 

  • INTEC Inc. (2012). Asbestos detection device, Japanese patent 5097668 (in Japanese).

  • Inoue, Y., Kaga, A., Yamaguchi, K., & Kamoi, S. (1998). Development of an automatic system for counting asbestos fibers using image processing. Particulate Science and Technology, 16, 263–279.

    Article  CAS  Google Scholar 

  • Inoue, Y., Kaga, A., & Yamaguchi, K. (1999). Cross-check between automatic counting system and visual counting facilities of asbestos fibers. Journal of Aerosol Research, Japan, 14, 129–137.

    Google Scholar 

  • Ishida, T., Alexandrov, M., Nishimura, T., Minakawa, K., Hirota, R., et al. (2010). Selective detection of airborne asbestos fibers using protein-based fluorescent probes. Environmental Science & Technology, 44(2), 755–759.

    Article  CAS  Google Scholar 

  • Ishida, T., Alexandrov, M., Nishimura, T., Minakawa, K., Hirota, R., et al. (2012). Evaluation of sensitivity of fluorescence-based asbestos detection by correlative microscopy. Journal of Fluorescence, 22(1), 357–363.

    Article  CAS  Google Scholar 

  • Ishida, T., Alexandrov, M., Nishimura, T., Hirota, R., Ikeda, T., & Kuroda, A. (2013). Molecular engineering of a fluorescent bioprobe for sensitive and selective detection of amphibole asbestos. PLoS ONE, 8(9), e76231.

    Article  CAS  Google Scholar 

  • Ishizu, K., Takemura, H., Kawabata, K., Asama, H., Mishima, T., & Mizoguchi, H. (2010). Automatic counting robot development supporting qualitative asbestos analysis: Asbestos, air bubbles, and particles classification using machine learning. Journal of Robotics and Mechatronics, 22, 506–513.

    Google Scholar 

  • Kawabata, K., Morishita, S., Takemura, H., Hotta, K., Mishima, T., Asama, H., et al. (2009). Development of an automated microscope for supporting qualitative asbestos analysis by dispersion staining. Journal of Robotics and Mechatronics, 21, 186–192.

    Google Scholar 

  • Kenny, L. C. (1984). Asbestos fibre counting by image analysis—The performance of the manchester asbestos program on magiscan. The Annals of Occupational Hygiene, 28, 401–415.

    Article  CAS  Google Scholar 

  • Kuroda, A., Nishimura, T., Ishida, T., Hirota, R., & Nomura, K. (2008). Detection of chrysotile asbestos by using a chrysotile-binding protein. Biotechnology and Bioengineering, 99(2), 285–289.

    Article  CAS  Google Scholar 

  • Mossman, B. T., Bignon, J., Corn, M., Seaton, A., & Gee, J. B. (1990). Asbestos: scientific developments and implications for public policy. Science, 247(4940), 294–301.

    Article  CAS  Google Scholar 

  • National Institute of Occupational Safety and Health (NIOSH) (1994) Asbestos and other fibers by PCM: Method 7400. In: NIOSH manual of analytical methods. Washington, DC: NIOSH. 2nd issue.

  • National Institute of Occupational Safety and Health (NIOSH). (2011). Current intelligence bulletin 62: Asbestos fibers and other elongate mineral particles: State of the science and roadmap for research. http://www.cdc.gov/niosh/docs/2011-159/pdfs/ 2011–159.pdf. Accessed 30 January 2014.

  • Taylor, D. G., Baron, P. A., Shulman, S. A., & Carter, J. W. (1984). Identification and counting of asbestos fibers. American Industrial Hygiene Association Journal, 45, 84–88.

    Article  CAS  Google Scholar 

  • World Health Organization (WHO) (2000) Air quality guidelines for Europe, 2nd edition. Copenhagen: WHO Regional Publications. http://www.euro.who.int/__data/assets/pdf_file/0005/74732/E71922.pdf. Accessed 30 January 2014.

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Acknowledgments

This work was supported by the Development of Systems and Technology for Advanced Measurement and Analysis Program of the Japan Science and Technology Agency. The sample collection was partially supported by the Ministry of the Environment, Japan, through the Environment Research and Technology Development Fund (5-1401).

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Authors and Affiliations

  1. Department of Molecular Biotechnology, Graduate School of Advanced Sciences of Matter, Hiroshima University, Higashi Hiroshima, Hiroshima, 739-8530, Japan

    Maxym Alexandrov, Tomoki Nishimura, Takenori Ishida, Ryuichi Hirota, Takeshi Ikeda & Akio Kuroda

  2. Advanced Technology Research and Development Institute, INTEC Inc., Yokohama, Kanagawa, 221-8520, Japan

    Etsuko Ichida, Kousuke Aoki & Tetsuo Kawasaki

Authors
  1. Maxym Alexandrov
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  2. Etsuko Ichida
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  3. Tomoki Nishimura
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  4. Kousuke Aoki
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  5. Takenori Ishida
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  6. Ryuichi Hirota
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  7. Takeshi Ikeda
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  8. Tetsuo Kawasaki
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  9. Akio Kuroda
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Corresponding author

Correspondence to Akio Kuroda.

Additional information

M. Alexandrov, E. Ichida, and T. Nishimura have equal contributions to this work.

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Alexandrov, M., Ichida, E., Nishimura, T. et al. Development of an automated asbestos counting software based on fluorescence microscopy. Environ Monit Assess 187, 4166 (2015). https://doi.org/10.1007/s10661-014-4166-y

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