Journal of Korean Society of Occupational and Environmental Hygiene (한국산업보건학회지)

Korean Industrial Hygiene Association (한국산업보건학회)
권호 표지
DOI QR코드

DOI QR Code

Developing Image Processing Program for Automated Counting of Airborne Fibers

이미지 처리를 통한 공기 중 섬유의 자동계수 알고리즘 프로그램 개발

  • 최성원 (가톨릭대학교 의과대학 예방의학교실) ;
  • 이희공 (켐익 코퍼레이션) ;
  • 이종일 (켐익 코퍼레이션) ;
  • 김현욱 (가톨릭대학교 의과대학 예방의학교실)
  • Received : 2014.12.03
  • Accepted : 2014.12.27
  • Published : 2014.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Objectives: An image processing program for asbestos fibers analyzing the gradient components and partial linearity was developed in order to accurately segment fibers. The objectives were to increase the accuracy of counting through the formulation of the size and shape of fibers and to guarantee robust fiber detection in noisy backgrounds. Methods: We utilized samples mixed with sand and sepiolite, which has a similar structure to asbestos. Sample concentrations of 0.01%, 0.05%, 0.1%, 0.5%, 1%, 2%, and 3%(w/w) were prepared. The sand used was homogenized after being sieved to less than $180{\mu}m$. Airborne samples were collected on MCE filters by utilizing a personal pump with 2 L/min flow rate for 30 minutes. We used the NIOSH 7400 method for pre-treating and counting the fibers on the filters. The results of the NIOSH 7400 method were compared with those of the image processing program. Results: The performance of the developed algorithm, when compared with the target images acquired by PCM, showed that the detection rate was on average 88.67%. The main causes of non-detection were missing fibers with a low degree of contrast and overlapping of faint and thin fibers. Also, some duplicate countings occurred for fibers with breaks in the middle due to overlapping particles. Conclusions: An image detection algorithm that could increase the accuracy of fiber counting was developed by considering the direction of the edge to extract images of fibers. It showed comparable results to PCM analysis and could be used to count fibers through real-time tracking by modeling a branch point to graph. This algorithm can be utilized to measure the concentrations of asbestos in real-time if a suitable optical design is developed.

Keywords

References

  1. Abell M, Shulman S. A, Baron P. A. The Quality of Fiber Count Data App. Ind. Hyg. 1989;4:283-285
  2. Baron, P.A. Measurement of airborne Fibers: A review. Ind. Health 2001;39:39-50. https://doi.org/10.2486/indhealth.39.39
  3. Burns JB, Hanson AR, Riseman EM. Extracting Straight Lines. IEEE Trans. Pattern Analysis and Machine Intelligence. 1986;8(4):425-455.
  4. Cho MC, Yoon S, Han H, Kim JK. Automated Counting of Airborne Asbestos Fibers by a High-Throughput Microscopy (HTM) Method, Sensors. 2011;11:7231-7242 https://doi.org/10.3390/s110707231
  5. Etemadi A. Robust segmentation of edge data. Int. Conf. on Image Processing and its Applications. 1992: 311-314
  6. Grompone von Gioi R, Jakubowicz J, Morel JM, Randall G. LSD: A fast line segment detector with a false detection control. 2010;32:722-732 https://doi.org/10.1109/TPAMI.2008.300
  7. Inoue Y, Kaga A, Yamaguchi K, Kamoi S. Development of an automatic system for counting asbestos fibers using image processing. Part. Sci. Technol. 1998;16: 263-279. https://doi.org/10.1080/02726359808906799
  8. Ishizu K, Takemura H, Kawabata K, Asama H, Mishima, T, Mizoguchi H. Image Processing of Particle Detection for Asbestos Qualitative Analysis Support Method - Particle Counting System Based on Classification of Background Area. Proceedings of International Conference on Control Automation Robotics and Vision. 2008;868-873
  9. Ishizu K, Takemura H, Kawabata K, Asama H, Mishima T, Mizoguchi H. Automatic Counting Robot Development Supporting Qualitative Asbestos Analysis - Asbestos, Air Bubbles, and Particles Classification Using Machine Learning -. Journal of Robotics and Mechatronics. 2010;22(4):506-513 https://doi.org/10.20965/jrm.2010.p0506
  10. Kawabata K, Komori Y, Mishima T, Asama H. An asbestos fiber detection technique utilizing image processing based on dispersion color. Part. Sci. Technol. 2009;27:177-192. https://doi.org/10.1080/02726350902776259
  11. Kenny LC. Asbestos fibre counting by image analysis-the performance of the Manchester asbestos program on Magiscan. Ann Occup Hyg. 1984;28:401-415 https://doi.org/10.1093/annhyg/28.4.401
  12. NIOSH : Manual of Analytical Methods, 3rd ed., Washington D.C., DHHS(NIOSH) Pub. 1989; 84-100
  13. Theodosiu Z, Tsapatsoulis N, Bujak-Pietrek S, Szadkowska-Stanczyk. Airborne asbestos fibers detection in microscope images using re-initialization free active contours. Conf. Proc. IEEE Eng Med Biol. Soc. 2010:4785-4788
  14. Yoshitaka M, Kazuhiro H, Haruhisa T. Asbestos Detection from Microscope Images Using Support Vector Random Field of Local Color Features, Advances in Neuro-Information Processing, Springer Berlin. 2009; 5507:344-352.