Automated droplet measurement (ADM): an enhanced video processing software for rapid droplet measurements
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10404_2016_Article_1722.pdf
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1.75 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Nguyen, Nam-Trung
Tor, Shu Beng
Gañán-Calvo, Alfonso M.
Loh, Ngiap Hiang
Tan, Say Hwa
Chong, Zhuang Jie
Date Issued
April 2016
Journal
Microfluidics and Nanofluidics
Publisher
Springer Berlin Heidelberg
Citation
Chong, Zhuang Zhi et al. “Automated Droplet Measurement (ADM): An Enhanced Video Processing Software for Rapid Droplet Measurements.” Microfluidics and Nanofluidics 20.4 (2016): n. pag.
Version
Author's final manuscript
Abstract
This paper identifies and addresses the bottlenecks that hamper the currently available software to perform in situ measurement on droplet-based microfluidic. The new and more universal object-based background extraction operation and automated binary threshold value selection make the processing step of our video processing software (ADM) fully automated. The ADM software, which is based on OpenCV image processing library, is made to perform measurements with high processing speed using efficient code. As the processing speed is higher than the data transfer speed from the video camera to permanent storage of computer, we integrate the camera software development kit (SDK) with ADM. The integration allows simultaneous operations of the video transfer/streaming and the video processing. As a result, the total time for droplet measurement using the new process flow with the integrated program is shortened significantly. ADM is also validated by comparing with both manual analysis and DMV software. ADM will be publicly released as a free tool. The software can also be used on a video file or files without the integration with the camera SDK.
MIT Department
Singapore-MIT Alliance in Research and Technology (SMART)
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s10404-016-1722-5
