Digital postprocessing and image segmentation for objective analysis of colorimetric reactions

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

Published: 2020-12-08 DOI: 10.1038/s41596-020-00413-0

colorimetric reactions

image segmentation

digital postprocessing

ImageJ

low-cost detection

AI 解读

Designer DNA nanostructures for viral inhibition

Tutorial: practical considerations for tissue clearing and imaging

Surgical implantation of wireless, battery-free optoelectronic epidural implants for optogenetic manipulation of spinal cord circuits in mice

In vivo and ex vivo electrophysiological study of the mouse heart to characterize the cardiac conduction system, including atrial and ventricular vulnerability

Norovirus detection in water samples at the level of single virus copies per microliter using a smartphone-based fluorescence microscope

Author Correction: Establishment of patient-derived cancer organoids for drug-screening applications

Graphene- and metal-induced energy transfer for single-molecule imaging and live-cell nanoscopy with (sub)-nanometer axial resolution

Design and simulation of DNA, RNA and hybrid protein–nucleic acid nanostructures with oxView

Synthesis of68Ga-radiopharmaceuticals using both generator-derived and cyclotron-produced68Ga as exemplified by [68Ga]Ga-PSMA-11 for prostate cancer PET imaging

Biotemplated precise assembly approach toward ultra-scaled high-performance electronics

查看全部

Sections

Figures

References

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, Raspberr
Introduction
Materials
- Equipment
Procedure
Troubleshooting
Timing
Anticipated results
References
ACKNOWLEDGMENTS

Lee, B. B. The evolution of concepts of color vision. Neurociencias 4, 209–224 (2008).
Molday, R. S. & Moritz, O. L. Photoreceptors at a glance. J. Cell Sci. 128, 4039–4045 (2015).
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).
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).
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).
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).
Rueden, C. T. et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics 18, 529 (2017).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676 (2012).
Roels, J. et al. An interactive ImageJ plugin for semi-automated image denoising in electron microscopy. Nat. Commun. 11, 1–13 (2020).
Boudaoud, A. et al. FibrilTool, an ImageJ plug-in to quantify fibrillar structures in raw microscopy images. Nat. Protoc. 9, 457 (2014).
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).
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).
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).
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).
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).
Russell, R. A. et al. Segmentation of fluorescence microscopy images for quantitative analysis of cell nuclear architecture. Biophys. J. 96, 3379–3389 (2009).
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).
Perez, A. J. et al. A workflow for the automatic segmentation of organelles in electron microscopy image stacks. Front. Neuroanat. 8, 126 (2014).
Stegmaier, J. et al. Fast segmentation of stained nuclei in terabyte-scale, time resolved 3D microscopy image stacks. PLoS ONE 9, e90036 (2014).
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).
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).
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).
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).
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).
Kortli, S. et al. Yersinia pestis detection using biotinylated dNTPs for signal enhancement in lateral flow assays. Anal. Chim. Acta 1112, 54–61 (2020).
Adkins, J. A. et al. Colorimetric and electrochemical bacteria detection using printed paper-and transparency-based analytic devices. Anal. Chem. 89, 3613–3621 (2017).
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).
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).

Digital postprocessing and image segmentation for objective analysis of colorimetric reactions

推荐阅读