Development of an automated asbestos counting software based on fluorescence microscopy

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.

[1]  John V. Crable and David G. Taylor,et al.  NIOSH manual of analytical methods , 2013 .

[2]  Hiroshi Mizoguchi,et al.  Automatic Counting Robot Development Supporting Qualitative Asbestos Analysis -Asbestos, Air Bubbles, and Particles Classification Using Machine Learning- , 2010, J. Robotics Mechatronics.

[3]  Norihiko Kohyama,et al.  Evaluation of Sensitivity of Fluorescence-Based Asbestos Detection by Correlative Microscopy , 2011, Journal of Fluorescence.

[4]  Norihiko Kohyama,et al.  Selective detection of airborne asbestos fibers using protein-based fluorescent probes. , 2010, Environmental science & technology.

[5]  P. Baron,et al.  Measurement of airborne fibers: a review. , 2001, Industrial health.

[6]  Yoshio Inoue,et al.  Cross-check between Automatic Counting System and Visual Counting Facilities of Asbestos Fibers , 1999 .

[7]  L. C. Kenny,et al.  Asbestos fibre counting by image analysis , 1984 .

[8]  J. M. Davis,et al.  Mass and number of fibres in the pathogenesis of asbestos-related lung disease in rats. , 1978, British Journal of Cancer.

[9]  L C Kenny Asbestos fibre counting by image analysis--the performance of the Manchester Asbestos Program on Magiscan. , 1984, The Annals of occupational hygiene.

[10]  Katsuhito Yamaguchi,et al.  DEVELOPMENT OF AN AUTOMATIC SYSTEM FOR COUNTING ASBESTOS FIBERS USING IMAGE PROCESSING , 1998 .

[11]  A. Seaton,et al.  Asbestos: scientific developments and implications for public policy. , 1990, Science.

[12]  Hiroshi Mizoguchi,et al.  Development of an Automated Microscope for Supporting Qualitative Asbestos Analysis by Dispersion Staining , 2009, J. Robotics Mechatronics.

[13]  Donghee Lee,et al.  Selective Detection and Automated Counting of Fluorescently-Labeled Chrysotile Asbestos Using a Dual-Mode High-Throughput Microscopy (DM-HTM) Method , 2013, Sensors.

[14]  Tomoki Nishimura,et al.  Detection of chrysotile asbestos by using a chrysotile‐binding protein , 2008, Biotechnology and bioengineering.