Automated droplet measurement (ADM): an enhanced video processing software for rapid droplet measurements
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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.
KeywordsDroplet measurement Video processing OpenCV Automated measurement
The authors gratefully acknowledge the research support from the Singapore-MIT Alliance (SMA) program in Manufacturing Systems and Technology (MST) and the Singapore Ministry of Education (MOE) Tier 2 Grant (No. 2011-T2-1-0-36). Z.Z. Chong would also want to thank the support of Nanyang Technological University Research Scholarship.
- ADM (2015) Adm website. http://a-d-m.weebly.com/. Accessed 01 July 2015
- Bradski G (2008) Learning OpenCV: computer vision with the OpenCV library. O’Reilly, SebastopolGoogle Scholar
- Izquierdo-Guerra W, García-Reyes E (2010) Background division, a suitable technique for moving object detection. In: Progress in pattern recognition, image analysis, computer vision, and applications. Springer Science Media, pp 121–127Google Scholar
- Konry T, Golberg A, Yarmush M (2013) Live single cell functional phenotyping in droplet nano-liter reactors. Sci Rep 3. doi: 10.1038/srep03179
- Matuska S, Hudec R, Benco M (2012) The comparison of CPU time consumption for image processing algorithm in Matlab and OpenCV. In: 2012 ELEKTRO, Institute of Electrical and Electronics Engineers (IEEE)Google Scholar
- Nuno-Maganda MA, Morales-Sandoval M, Torres-Huitzil C (2011) A hardware coprocessor integrated with OpenCV for edge detection using cellular neural networks. In: 2011 sixth international conference on image and graphics. Institute of Electrical and Electronics Engineers (IEEE)Google Scholar
- Tan SH, Maes F, Semin B, Vrignon J, Baret JC (2014a) The microfluidic jukebox. Sci Rep 4. doi: 10.1038/srep04787