Treffer: Digital postprocessing and image segmentation for objective analysis of colorimetric reactions.
Title:
Digital postprocessing and image segmentation for objective analysis of colorimetric reactions.
Authors:
Woolf MS; Department of Chemistry, University of Virginia, Charlottesville, VA, USA. msw2s@virginia.edu., Dignan LM; Department of Chemistry, University of Virginia, Charlottesville, VA, USA., Scott AT; Department of Chemistry, University of Virginia, Charlottesville, VA, USA., Landers JP; Department of Chemistry, University of Virginia, Charlottesville, VA, USA.; Department of Mechanical Engineering, University of Virginia, Charlottesville, VA, USA.; Department of Pathology, University of Virginia, Charlottesville, VA, USA.
Source:
Nature protocols [Nat Protoc] 2021 Jan; Vol. 16 (1), pp. 218-238. Date of Electronic Publication: 2020 Dec 09.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101284307 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1750-2799 (Electronic) Linking ISSN: 17502799 NLM ISO Abbreviation: Nat Protoc Subsets: MEDLINE
Imprint Name(s):
Original Publication: London, UK : Nature Pub. Group, 2006-
MeSH Terms:
References:
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Clark, C. P. et al. Closable valves and channels for polymeric microfluidic devices. Micromachines 11, 627 (2020). (PMID: 10.3390/mi11070627)
Tian, Z., Liu, L. Q., Peng, C., Chen, Z. & Xu, C. A new development of measurement of 19-Nortestosterone by combining immunochromatographic strip assay and ImageJ software. Food Agric. Immunol. 20, 1–10 (2009). (PMID: 10.1080/09540100802621017)
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Adkins, J. A. et al. Colorimetric and electrochemical bacteria detection using printed paper-and transparency-based analytic devices. Anal. Chem. 89, 3613–3621 (2017). (PMID: 10.1021/acs.analchem.6b05009)
Tanner, N. A., Zhang, Y. & Evans, T. C. Jr Visual detection of isothermal nucleic acid amplification using pH-sensitive dyes. Biotechniques 58, 59–68 (2015). (PMID: 10.2144/000114253)
Zhang, Y. et al. Rapid molecular detection of SARS-CoV-2 (COVID-19) virus RNA using colorimetric LAMP. Preprint at https://www.medrxiv.org/content/10.1101/2020.02.26.20028373v1 (2020).
Singh, H. K., Tomar, S. K. & Maurya, P. K. Thresholding techniques applied for segmentation of RGB and multispectral images. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.735.9363&rep=rep1&type=pdf (2012).
Molday, R. S. & Moritz, O. L. Photoreceptors at a glance. J. Cell Sci. 128, 4039–4045 (2015). (PMID: 10.1242/jcs.175687)
Jameson, K. A. in The Oxford Companion to Consciousness 155–158 (Oxford University Press, 2009).
Krauss, S. T. et al. Objective method for presumptive field-testing of illicit drug possession using centrifugal microdevices and smartphone analysis. Anal. Chem. 88, 8689–8697 (2016). (PMID: 10.1021/acs.analchem.6b01982)
CIE. Commission internationale de l’eclairage proceedings, 1931 (Cambridge University, 1932).
Smith, T. & Guild, J. The CIE colorimetric standards and their use. Trans. Opt. Soc. 33, 73 (1931). (PMID: 10.1088/1475-4878/33/3/301)
CIE. Colorimetry-Part 4: CIE 1976 L* a* b* colour space. International Organization for Standardization https://www.iso.org/standard/74166.html (2008).
Schnapf, J., Kraft, T. & Baylor, D. Spectral sensitivity of human cone photoreceptors. Nature 325, 439–441 (1987). (PMID: 10.1038/325439a0)
Anderson, M., Motta, R., Chandrasekar, S. & Stokes, M. in Color and Imaging Conference 238–245 (Society for Imaging Science and Technology, 1996).
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012). (PMID: 10.1038/nmeth.2089)
Rueden, C. T. et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics 18, 529 (2017). (PMID: 10.1186/s12859-017-1934-z)
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676 (2012). (PMID: 10.1038/nmeth.2019)
Roels, J. et al. An interactive ImageJ plugin for semi-automated image denoising in electron microscopy. Nat. Commun. 11, 1–13 (2020). (PMID: 10.1038/s41467-020-14529-0)
Boudaoud, A. et al. FibrilTool, an ImageJ plug-in to quantify fibrillar structures in raw microscopy images. Nat. Protoc. 9, 457 (2014). (PMID: 10.1038/nprot.2014.024)
Krauss, S. T., Holt, V. C. & Landers, J. P. Simple reagent storage in polyester-paper hybrid microdevices for colorimetric detection. Sens. Actuators B Chem. 246, 740–747 (2017). (PMID: 10.1016/j.snb.2017.02.018)
Krauss, S. T., Nauman, A. Q., Garner, G. T. & Landers, J. P. Color manipulation through microchip tinting for colorimetric detection using hue image analysis. Lab Chip 17, 4089–4096 (2017). (PMID: 10.1039/C7LC00796E)
Krauss, S. T. et al. Centrifugal microfluidic devices using low-volume reagent storage and inward fluid displacement for presumptive drug detection. Sens. Actuators B Chem. 284, 704–710 (2019). (PMID: 10.1016/j.snb.2018.12.113)
Thompson, B. L., Wyckoff, S. L., Haverstick, D. M. & Landers, J. P. Simple, reagentless quantification of total bilirubin in blood via microfluidic phototreatment and image analysis. Anal. Chem. 89, 3228–3234 (2017). (PMID: 10.1021/acs.analchem.7b00354)
Jackson, K. R. et al. A novel loop-mediated isothermal amplification method for identification of four body fluids with smartphone detection. Forensic Sci. Int. Genet. 45, 102195 (2020). (PMID: 10.1016/j.fsigen.2019.102195)
Russell, R. A. et al. Segmentation of fluorescence microscopy images for quantitative analysis of cell nuclear architecture. Biophys. J. 96, 3379–3389 (2009). (PMID: 10.1016/j.bpj.2008.12.3956)
Balsam, J., Bruck, H. A., Kostov, Y. & Rasooly, A. Image stacking approach to increase sensitivity of fluorescence detection using a low cost complementary metal-oxide-semiconductor (CMOS) webcam. Sens. Actuators B Chem. 171, 141–147 (2012). (PMID: 10.1016/j.snb.2012.02.003)
Perez, A. J. et al. A workflow for the automatic segmentation of organelles in electron microscopy image stacks. Front. Neuroanat. 8, 126 (2014). (PMID: 10.3389/fnana.2014.00126)
Stegmaier, J. et al. Fast segmentation of stained nuclei in terabyte-scale, time resolved 3D microscopy image stacks. PLoS ONE 9, e90036 (2014). (PMID: 10.1371/journal.pone.0090036)
Capitan-Vallvey, L. F., Lopez-Ruiz, N., Martinez-Olmos, A., Erenas, M. M. & Palma, A. J. Recent developments in computer vision-based analytical chemistry: a tutorial review. Anal. Chim. Acta 899, 23–56 (2015). (PMID: 10.1016/j.aca.2015.10.009)
Cao, S., Huang, D., Wang, Y. & Li, G. in Advances in Mechanical and Electronic Engineering 381–386 (Springer, 2012).
Cantrell, K., Erenas, M., de Orbe-Payá, I. & Capitán-Vallvey, L. Use of the hue parameter of the hue, saturation, value color space as a quantitative analytical parameter for bitonal optical sensors. Anal. Chem. 82, 531–542 (2010). (PMID: 10.1021/ac901753c)
Ibraheem, N. A., Hasan, M. M., Khan, R. Z. & Mishra, P. K. Understanding color models: a review. ARPN J. Sci. Technol. 2, 265–275 (2012).
Hanbury, A. The taming of the hue, saturation and brightness colour space. Proc. 7th Computer Vision Winter Workshop http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.2574 (2002).
Barthel, K. U. 3D-data representation with ImageJ. Proc. 1st ImageJ User & Developer Conference http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.725 (2006).
Clark, C. P. et al. Closable valves and channels for polymeric microfluidic devices. Micromachines 11, 627 (2020). (PMID: 10.3390/mi11070627)
Tian, Z., Liu, L. Q., Peng, C., Chen, Z. & Xu, C. A new development of measurement of 19-Nortestosterone by combining immunochromatographic strip assay and ImageJ software. Food Agric. Immunol. 20, 1–10 (2009). (PMID: 10.1080/09540100802621017)
Hwang, J., Kwon, D., Lee, S. & Jeon, S. Detection of Salmonella bacteria in milk using gold-coated magnetic nanoparticle clusters and lateral flow filters. RSC Adv. 6, 48445–48448 (2016). (PMID: 10.1039/C6RA05446C)
Kortli, S. et al. Yersinia pestis detection using biotinylated dNTPs for signal enhancement in lateral flow assays. Anal. Chim. Acta 1112, 54–61 (2020). (PMID: 10.1016/j.aca.2020.03.059)
Adkins, J. A. et al. Colorimetric and electrochemical bacteria detection using printed paper-and transparency-based analytic devices. Anal. Chem. 89, 3613–3621 (2017). (PMID: 10.1021/acs.analchem.6b05009)
Tanner, N. A., Zhang, Y. & Evans, T. C. Jr Visual detection of isothermal nucleic acid amplification using pH-sensitive dyes. Biotechniques 58, 59–68 (2015). (PMID: 10.2144/000114253)
Zhang, Y. et al. Rapid molecular detection of SARS-CoV-2 (COVID-19) virus RNA using colorimetric LAMP. Preprint at https://www.medrxiv.org/content/10.1101/2020.02.26.20028373v1 (2020).
Singh, H. K., Tomar, S. K. & Maurya, P. K. Thresholding techniques applied for segmentation of RGB and multispectral images. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.735.9363&rep=rep1&type=pdf (2012).
Substance Nomenclature:
0 (Coloring Agents)
Entry Date(s):
Date Created: 20201210 Date Completed: 20210217 Latest Revision: 20220417
Update Code:
20250114
DOI:
10.1038/s41596-020-00413-0
PMID:
33299153
Database:
MEDLINE
Weitere Informationen
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.