부산대학교 도서관

Fluorescence microscopes are widely used to detect fluore-scent objects in a biological sample. However, traditional benchtop fluorescence microscopes are limited to high cost and nonportability. Recently, smartphone-based fluorescence microscopes (SFMs) have been developed, which capture images using a smartphone camera with the help of an external optical device and serve as an inexpensive and portable alternative. However, to analyze the images and get the accurate count of bioparticles, external software and computational resources are required; moreover, the lack of single and clustered count capabilities in SFMs without relying on an image library limits the use of SFMs. Here, we present an automated method as a mobile application with the ability to provide the bioparticle counts at the point of care by imaging human leukocytes and fluorescent microparticles. The application employs efficient image processing techniques that result in faster automated counting and reduced computational cost while providing comparable counts to industrial and academic standards. Compared with counting results calculated by ImageJ, a correlation coefficient of ${{R}^2}$ > 0.99 was observed along with the average relative error of ∼0.5% on different kinds of fluorescent images.