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Portable Environmental Microfluidic Chips with Colorimetric Sensors: Image Recognition and Visualization

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Abstract

Objective: The aim of this study was to develop a method to improve color pattern recognition and visual detection of a portable environmental microfluidic chip.

Methods & Results: Given a sensing image obtained from the chip, pillars were distinguished from the image background, each having their own sensing result. Each image was binarized to clarify the border of each pillar, and the K-means clustering algorithm was applied. For each identified pillar, a red, green, and blue (RGB) histogram was extracted, and a similarity comparison was performed to find reference values close to each given input. The identified results were displayed in 3-dimensional (3-D) space using the hue, saturation, and value (HSV) model to show the differences between each input and reference value from the chip more clearly.

Conclusion: Image recognition can be effectively used to display the results generated from a portable environmental microfluidic chip as a colorimetric sensor device.

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Acknowledgments

The research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF-2016R1D1A1A02937456) and by the Commercialization Promotion Agency for R&D Outcomes (2018K000370&2019K000606).

Author information

Correspondence to Myoung-Hwan Park.

Ethics declarations

This article does not contain any studies with human participants or animals performed by any of the authors.

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Conflict of Interest

Hyeon-Gyu Kim declares that he has no conflicts of interest. Yang Woo Yu declares that he has no conflicts of interest. Yooyeol Yang declares that he has no conflicts of interest. Myoung-Hwan Park declares that he has no conflicts of interest.

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Kim, H., Yu, Y.W., Yang, Y. et al. Portable Environmental Microfluidic Chips with Colorimetric Sensors: Image Recognition and Visualization. Toxicol. Environ. Health Sci. 11, 320–326 (2019) doi:10.1007/s13530-019-0419-z

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Keywords

  • Microfluidic chip
  • Image recognition
  • K-means clustering
  • Binarization
  • Visualization