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Access Type
WSU Access
Date of Award
January 2017
Degree Type
Thesis
Degree Name
M.S.
Department
Electrical and Computer Engineering
First Advisor
Amar S. Basu
Abstract
Droplet microfluidics, a subset of microfluidics in which water-in-oil emulsions are used as reaction containers for high throughput biological screening protocols. Existing tools to analyze the contents of droplets are based primarily on laser-based flow cytometry, which has high throughput but limited spatial resolution for differentiating the contents of droplets. Visual inspection by microscopy can give the researcher more detailed information about contents, but it is time consuming. Thus, by automating visual inspection using computer machine vision, one can obtain high definition data while also maintaining throughput. Past efforts at developing computer vision-based droplet analysis have limited throughput due to either software constraints (interpreted language – MATLAB) or hardware constraints (serial processing CPUs). This paper proposes a high throughput droplet analysis algorithm, implemented on the GPU, achieving speeds of ~3000 frames per second, making real-time analysis possible. Real time analysis can be applied to a variety of droplet-based protocols, including polydispersity measurements for digital assays, quantifying cell encapsulation, shape-based chemical detection, and others.
Recommended Citation
Vedhanayagam, Arpith, "Droplet Tracking At Speeds 3000 Frames Per Second Using Gpu Accelerated Image Processing" (2017). Wayne State University Theses. 647.
https://digitalcommons.wayne.edu/oa_theses/647