Digital postprocessing and image segmentation for objective analysis of colorimetric reactions
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 ca...
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| Veröffentlicht in: | Nature protocols 2021-01, Vol.16 (1), p.218-238 |
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| creator | Woolf, M. Shane Dignan, Leah M. Scott, Anchi T. Landers, James P. |
| description | 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 |
| doi_str_mv | 10.1038/s41596-020-00413-0 |
| format | Article |
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Shane</au><au>Dignan, Leah M.</au><au>Scott, Anchi T.</au><au>Landers, James P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Digital postprocessing and image segmentation for objective analysis of colorimetric reactions</atitle><jtitle>Nature protocols</jtitle><stitle>Nat Protoc</stitle><addtitle>Nat Protoc</addtitle><date>2021-01-01</date><risdate>2021</risdate><volume>16</volume><issue>1</issue><spage>218</spage><epage>238</epage><pages>218-238</pages><issn>1754-2189</issn><eissn>1750-2799</eissn><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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| subjects | 631/1647/2196/2197 631/1647/794 639/638/11 639/638/45 Algorithms Analysis Analytical Chemistry Applications programs Biological assay Biological Techniques Biomedical and Life Sciences Cameras Color Colorimetric analysis Colorimetry Colorimetry - instrumentation Colorimetry - methods Coloring Agents - analysis Computational Biology/Bioinformatics Computer programming Computer programs Computer vision Digital cameras Digital imaging Digitization Equipment Design Fluorescence Image analysis Image processing Image Processing, Computer-Assisted - instrumentation Image Processing, Computer-Assisted - methods Image segmentation Lab-On-A-Chip Devices Life Sciences Methods Microarrays Optical properties Organic Chemistry Pattern recognition Protocol Robustness (mathematics) Scanners Signal processing Smartphones Software Technology application Workflow |
| title | Digital postprocessing and image segmentation for objective analysis of colorimetric reactions |
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