## 3 Results and Discussion
### Automated and High-Throughput Counting of Chrysotile Using DM-HTM  
Our DM-HTM device was able to acquire both reflection and fluorescence images consecutively at the same positions of the sample slide. Additional light source and fluorescence filters were added to the HTM device, and the scan pathway was changed to the regression mode in order to return to the position where the first reflection image was taken, and to start acquiring fluorescence images. The capabilities of the DM-HTM system were tested by running the macro that automates a series of  
Figure 5: Reflection and fluorescence images of mixed samples of chrysotile and amosite at various concentration ratios. Reflection (left column) and fluorescence images (right column; pseudo-colored red). Concentration ratio of chrysotile to amosite: (**a**) 200/0, (**b**) 100/100, (**c**) 50/150, and (**d**) 25/175 \(\,\mathrm{\SIUnitSymbolMicro mL}\). \(\mathrm{Bar}=100\)\(\,\mathrm{\SIUnitSymbolMicro m}\). built-in commands and plugins provided by the ImageJ. Figure 6(a) shows the result of counting the serially diluted fluorescent microbeads. The slope of the linear regression line through the origin is 0.89 (\(R^{2}=0.99\)) denoting that the measured number of particles (\(N_{\text{measured}}\)) is about 10% less than the theoretical number of particles (\(N_{\text{theory}}\)), presumably due to sample loss during pipetting and uneven distribution of particles in the chamber. Chrysotile samples that were diluted in four steps were prepared to prove the possibility of enhancing the sensitivity of detecting asbestos fibers in the fluorescence mode. We acquired reflection images of highly diluted samples at low intensity level of illumination deliberately making the images have lower contrast. The results from the analysis of both reflection and fluorescence images by DM-HTM were compared with the results from the manual counting method described in Section 2.6 and presented in Figure 6(b). Manual count represents the total number of fibers enumerated from a set of 50 reflection images by a human researcher and is considered to be the gold standard in this study. According to the slopes of the linear regression lines in Figure 6(b), we found that the automatic counts from reflection and fluorescence image analyses were 10% less and 25% greater than the manual counts, respectively. Although neither method accurately reproduces the manual counting, the correlation with the manual counts is higher for the reflection image analysis. This difference could be caused by uncounted small fibers and fibers with low contrast against the background during manual and automatic analyses of the bright-field reflection images (not shown here). Those undetectable small and low contrast chrysotile fibers can be seen and identified more sensitively in the fluorescence images resulting in automatic counts biased by 25% from the manual counts. The extent of bias is not peculiar to the mode of imaging but dependent on the quality of sample images and image processing scheme as well. Therefore standardization of the testing procedures from sample preparation to image acquisition and analysis is necessary to reduce the bias or to render the bias reproducible. The contrast of detecting asbestos fibers in the bright-field mode can be greatly enhanced with a differential interference contrast microscopy as reported in a recent study [17]. Figure 6: Validation of serially diluted microbead and fiber counts by automated image processing and analysis. (**a**) Comparison of theoretical (\(N_{\text{theory}}\)) and measured (\(N_{\text{measured}}\)) numbers of microspheres in fluorescence mode of DM-HTM method. (**b**) Comparison of chrysotile asbestos fiber counts determined by DM-HTM and manual counting. We also evaluated our DM-HTM with mixed asbestos samples as described in Section 2.4.