Digital postprocessing and image segmentation for objective analysis of colorimetric reactions

• Published in: Nature protocols Vol. 16; no. 1; pp. 218 - 238
• Main Authors: Woolf, M. Shane, Dignan, Leah M., Scott, Anchi T., Landers, James P.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.01.2021; Nature Publishing Group
• 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; United States
• ISSN: 1754-2189; 1750-2799; 1750-2799
• DOI: 10.1038/s41596-020-00413-0

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. This protocol provides ImageJ-based workflows for the analysis of images obtained from colorimetric assays. New users can take advantage of a basic workflow; more experienced users can benefit from more advanced analysis procedures.