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http://dx.doi.org/10.15269/JKSOEH.2014.24.4.484

Developing Image Processing Program for Automated Counting of Airborne Fibers  

Choi, Sungwon (Dept. of Prev. Med, College of Medicine, The Catholic University of Korea)
Lee, Heekong (Kemik coporation)
Lee, Jong Il (Kemik coporation)
Kim, Hyunwook (Dept. of Prev. Med, College of Medicine, The Catholic University of Korea)
Publication Information
Journal of Korean Society of Occupational and Environmental Hygiene / v.24, no.4, 2014 , pp. 484-491 More about this Journal
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
Asbestos; automated counting; image processing;
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