Development of an imaging method for quantifying a large digital PCR droplet

Jen-Yu Huang; Shu-Sheng Lee; Yu-Hsiang Hsu

doi:10.1117/12.2251801

17 February 2017 Development of an imaging method for quantifying a large digital PCR droplet

Jen-Yu Huang, Shu-Sheng Lee, Yu-Hsiang Hsu

Proceedings Volume 10072, Optical Diagnostics and Sensing XVII: Toward Point-of-Care Diagnostics; 100720I (2017) https://doi.org/10.1117/12.2251801
Event: SPIE BiOS, 2017, San Francisco, California, United States

Abstract

Portable devices have been recognized as the future linkage between end-users and lab-on-a-chip devices. It has a user friendly interface and provides apps to interface headphones, cameras, and communication duct, etc. In particular, the digital resolution of cameras installed in smartphones or pads already has a high imaging resolution with a high number of pixels. This unique feature has triggered researches to integrate optical fixtures with smartphone to provide microscopic imaging capabilities. In this paper, we report our study on developing a portable diagnostic tool based on the imaging system of a smartphone and a digital PCR biochip. A computational algorithm is developed to processing optical images taken from a digital PCR biochip with a smartphone in a black box. Each reaction droplet is recorded in pixels and is analyzed in a sRGB (red, green, and blue) color space. Multistep filtering algorithm and auto-threshold algorithm are adopted to minimize background noise contributed from ccd cameras and rule out false positive droplets, respectively. Finally, a size-filtering method is applied to identify the number of positive droplets to quantify target’s concentration. Statistical analysis is then performed for diagnostic purpose. This process can be integrated in an app and can provide a user friendly interface without professional training.

Citation Download Citation

Jen-Yu Huang, Shu-Sheng Lee, and Yu-Hsiang Hsu "Development of an imaging method for quantifying a large digital PCR droplet", Proc. SPIE 10072, Optical Diagnostics and Sensing XVII: Toward Point-of-Care Diagnostics, 100720I (17 February 2017); https://doi.org/10.1117/12.2251801

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