## 1 Introduction  
Asbestos is a type of silica compound comprised of microscopic bundles of silicate fibers that can easily become airborne [1]. Asbestos has several advantages, including durability, heat resistance, and flame resistance; therefore, it has been used widely as a construction material. However, since it is reported that accumulation of asbestos in the body causes serious diseases such as lung cancer, malignant mesothelioma, and other respiratory symptoms, its use is prohibited in most developed countries. Despite regulations, asbestos contamination still remains to be a common problem and many people die from exposure to asbestos worldwide [2]. There are several established methods to detect asbestos, including phase-contrast microscopy (PCM) [3], polarized microscopy (PLM) [4], X-ray diffraction analysis and transmission electron microscopy (TEM) [5]. Although PCM is simple and cost-effective compared to other methods, it has some limitations. PCM cannot detect thin fibers with diameters less than 0.25 \(\upmu\)m due to a resolution limit, and it cannot clearly identify certain types of asbestos [6]. However, asbestos fiber dimension is an important factor in determining respiratory disease risk. Both lung cancer and asbestos have been strongly associated with exposure to thin asbestos fibers (\(<\)0.25 \(\upmu\)m) [7]. In addition, the PCM method gives inaccurate results due to the subjective analysis of the operator. Relatively accurate methods, including EM, also require trained experts and expensive equipments. Thus, innovative techniques for detecting asbestos are being studied in many areas; for example, automated image analysis techniques to replace the conventional manual counting method have shown remarkable results. These methods are mostly based on the color dispersion of asbestos. [PERSON] _et al._[8] developed a qualitative asbestos detection method by modifying conventional methods, and they attempted to detect only asbestos fibers among many types of particles by using both color dispersion and shape information. The use of refraction phenomena or polarization also allows asbestos to be distinguished from other particles. [PERSON] _et al._[9] and [PERSON] _et al._[10] attempted to automate the detection of asbestos by \"image matching,\" which uses dispersion staining to search for color changes between two images in order to identify the asbestos. However, due to the complicated process, their methods required enormous time for image processing and analysis, and inconspicuous color changes remained difficult to detect. An automated image acquisition system is another indispensable requirement for the development of automated airborne asbestos counting method. In our previous study [11], we developed a high-throughput microscopy (HTM) method and demonstrated the feasibility of automated image acquisition and counting of asbestos-like fibers by comparing HTM results with conventional PCM. The proposed HTM method reduces the analysis time significantly from \(\sim\)90 min to \(<\)5 min using a batch process executing a list of built-in commands of public domain freeware ImageJ, despite the fact that the field-of-view is enlarged by \(\sim\)40-fold from 0.00785 mm\({}^{2}\) to 0.3185 mm\({}^{2}\). Accuracy of the HTM method was reasonably comparable to PCM when tested on Proficiency Analytical Testing (PAT) standard asbestos samples. A common limitation of the PCM and HTM methods, both of which are based on bright-field imaging, is their inability to distinguish asbestos from non-asbestos fibrous materials. In a recent study, [PERSON] _et al._[12] implanted a biotechnology into asbestos detection technique. They used DksA, a chrysotile-binding protein, to quantitatively detect chrysotile, based on a colorimetric method by using DksA-AP, and they also used DksA-GFP for fluorescence imaging.