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Digital postprocessing and image segmentation for objective analysis of colorimetric reactions

Nature Protocols volume 16, pages 218–238 (2021)Cite this article

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Abstract

Recently, there has been an explosion of scientific literature describing the use of colorimetry for monitoring the progression or the endpoint result of colorimetric reactions. The availability of inexpensive imaging technology (e.g., scanners, Raspberry Pi, smartphones and other sub-$50 digital cameras) has lowered the barrier to accessing cost-efficient, objective detection methodologies. However, to exploit these imaging devices as low-cost colorimetric detectors, it is paramount that they interface with flexible software that is capable of image segmentation and probing a variety of color spaces (RGB, HSB, Y’UV, L*a*b*, etc.). Development of tailor-made software (e.g., smartphone applications) for advanced image analysis requires complex, custom-written processing algorithms, advanced computer programming knowledge and/or expertise in physics, mathematics, pattern recognition and computer vision and learning. Freeware programs, such as ImageJ, offer an alternative, affordable path to robust image analysis. Here we describe a protocol that uses the ImageJ program to process images of colorimetric experiments. In practice, this protocol consists of three distinct workflow options. This protocol is accessible to uninitiated users with little experience in image processing or color science and does not require fluorescence signals, expensive imaging equipment or custom-written algorithms. We anticipate that total analysis time per region of interest is ~6 min for new users and <3 min for experienced users, although initial color threshold determination might take longer.

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Fig. 1: Color spaces and linearization of the hue.
Fig. 2: Anticipated results.
Fig. 3: Overview and general workflow of the protocol.
Fig. 4: Overview of the ImageJ GUI and ovoid ROI selection, refinement and cropping.
Fig. 5: Overview of rotational image transformation with rectangular ROI selection, refinement and cropping.
Fig. 6: Workflow B—The Crop-Threshold-and-Go method of segmentation and analysis.
Fig. 7: Workflow C—Overview of digital color balancing and color threshold application.
Fig. 8: Workflow A—The Crop-and-Go method of segmentation and analysis.
Fig. 9: Workflow A—The Crop-and-Go method of segmentation and analysis.
Fig. 10: Workflow C—The Crop-Tint-Threshold-and-Go method of segmentation and analysis.

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Data availability

All figures and any associated data have not been previously published. The source image files used to generate the primary data underlying the figures featured in this protocol are available via open access at zenodo.org, which can be found at https://doi.org/10.5281/zenodo.3976070.

Code availability

This protocol requires no custom scripts or algorithms. Rather, this protocol uses free, publicly available software packages (e.g., ImageJ 2.0.0-rc-69/1.52p bundled with Java 1.8.0_172 (32-bit) (https://imagej.nih.gov/ij/download.html) and the Fiji distribution of ImageJ (https://imagej.net/Fiji/Downloads))10,11,12 as well as available commercial software (e.g., Microsoft Excel for Microsoft 365).

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Acknowledgements

We gratefully acknowledge K. Siller (UVA Research Computing, University of Virginia) for his advice and instruction provided in ImageJ-Fiji workshops given at the University of Virginia (attended by M.S.W. and A.T.S. in 2018). We also recognize the initial foundational image analysis work of previous Landers lab members, including C. P. Clark, S. T. Krauss, K. R. Jackson, J. Li, D. A. Nelson, O. N. Scott and B. L. Thompson.

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Authors and Affiliations

  1. Department of Chemistry, University of Virginia, Charlottesville, VA, USA

    M. Shane Woolf, Leah M. Dignan, Anchi T. Scott & James P. Landers

  2. Department of Mechanical Engineering, University of Virginia, Charlottesville, VA, USA

    James P. Landers

  3. Department of Pathology, University of Virginia, Charlottesville, VA, USA

    James P. Landers

Authors
  1. M. Shane Woolf
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  2. Leah M. Dignan
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  3. Anchi T. Scott
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  4. James P. Landers
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Contributions

All author contributions are based on CRediT standards. Writing—original draft: M.S.W. Writing—review and editing: all authors. Conceptualization, methodology, investigation, data curation, formal analysis and visualization of the image segmentation procedure: M.S.W. Conceptualization, methodology, investigation and image acquisition for all colorimetric experiments: M.S.W., L.M.D. and A.T.S. Project administration and funding acquisition: J.P.L.

Corresponding author

Correspondence to M. Shane Woolf.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Bahram Hemmateenejad and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Clark, C. P. et al. Micromachines 11, 627 (2020): https://doi.org/10.3390/mi11070627

Jackson, K. R. et al. Forensic Sci. Int. Genet. 45, 102195 (2020): https://doi.org/10.1016/j.fsigen.2019.102195

Krauss, S. T. et al. Sens. Actuators B Chem. 284, 704−710 (2019): https://doi.org/10.1016/j.snb.2018.12.113

Krauss, S.T. et al. Lab Chip 17, 4089−4096 (2017): https://doi.org/10.1039/C7LC00796E

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Woolf, M.S., Dignan, L.M., Scott, A.T. et al. Digital postprocessing and image segmentation for objective analysis of colorimetric reactions. Nat Protoc 16, 218–238 (2021). https://doi.org/10.1038/s41596-020-00413-0

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