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Sensors 2014, 14(8), 15244-15261; doi:10.3390/s140815244

Optimization of a Cell Counting Algorithm for Mobile Point-of-Care Testing Platforms

1
Real-Time Cyber-Physical System Laboratory, Daegu Gyeoungbuk Institute of Science and Technology (DGIST), Daegu 711-873, Korea
2
Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
3
Cybernetics Laboratory, Daegu Gyeoungbuk Institute of Science and Technology (DGIST), Daegu 711-873, Korea
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 26 May 2014 / Revised: 4 August 2014 / Accepted: 8 August 2014 / Published: 19 August 2014
(This article belongs to the Section Biosensors)
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Abstract

In a point-of-care (POC) setting, it is critically important to reliably count the number of specific cells in a blood sample. Software-based cell counting, which is far faster than manual counting, while much cheaper than hardware-based counting, has emerged as an attractive solution potentially applicable to mobile POC testing. However, the existing software-based algorithm based on the normalized cross-correlation (NCC) method is too time- and, thus, energy-consuming to be deployed for battery-powered mobile POC testing platforms. In this paper, we identify inefficiencies in the NCC-based algorithm and propose two synergistic optimization techniques that can considerably reduce the runtime and, thus, energy consumption of the original algorithm with negligible impact on counting accuracy. We demonstrate that an AndroidTM smart phone running the optimized algorithm consumes 11.5× less runtime than the original algorithm. View Full-Text
Keywords: cell counting; point-of-care testing; normalized cross-correlation cell counting; point-of-care testing; normalized cross-correlation
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Ahn, D.; Kim, N.S.; Moon, S.; Park, T.; Son, S.H. Optimization of a Cell Counting Algorithm for Mobile Point-of-Care Testing Platforms. Sensors 2014, 14, 15244-15261.

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