{"title":"ColorI-DT:用于定量评估显微镜彩色图像差异的开源工具。","authors":"Filippo Piccinini, Michele Tritto, Jae-Chul Pyun, Misu Lee, Bongseop Kwak, Bosung Ku, Nicola Normanno, Gastone Castellani","doi":"10.1016/j.csbj.2025.06.019","DOIUrl":null,"url":null,"abstract":"<p><p>In several fields, quantitatively comparing color images is crucial. For instance, this is important in Histopathology, where different microscopes/cameras are typically used for visualizing patient samples by causing significant color variation. No ground-truth metric exists for estimating differences between pairs of color images. A range of possible solutions is available but there is no existing open-source tool that allow clinicians and researchers to apply these metrics to microscopy images through an intuitive, easy-to-use software. In this work, we developed <i>Color Image Difference Tool</i> (<i>ColorI-DT</i>), an open-source tool for measuring quantitative differences between color images of the same subject acquired under different settings. Thanks to a user-friendly graphical user interface, it allows the selection of a pair of color images and a metric from a list of available options, and produces an output 2D pixel-wise color difference matrix between corresponding pixels in the input images. The metrics currently implemented are: (<i>1</i>) Euclidean <math><mrow><mi>Δ</mi> <mi>E</mi></mrow> </math> ; (<i>2</i>) International Commission on Illumination (CIE) 76 (Luv); (<i>3</i>) CIE76 (Lab); (<i>4</i>) CIE94; (<i>5</i>) CIE00; (<i>6</i>) Colour Measurement Committee (CMC). To demonstrate how to use the tool, microscopy images with a predominant color in the red, green, or blue channel were used. In particular, we checked which among the 6 metrics displays the most predictable and linear behavior in the case of controlled primary color alterations. For more pronounced color adjustments, a qualitative comparison would be likely sufficient for analyzing color differences, as a quantitative tool may become unreliable due to the inherent limitations of the implemented metrics.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2526-2536"},"PeriodicalIF":4.4000,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12197881/pdf/","citationCount":"0","resultStr":"{\"title\":\"<i>ColorI-DT</i>: An open-source tool for the quantitative evaluation of differences in microscopy color images.\",\"authors\":\"Filippo Piccinini, Michele Tritto, Jae-Chul Pyun, Misu Lee, Bongseop Kwak, Bosung Ku, Nicola Normanno, Gastone Castellani\",\"doi\":\"10.1016/j.csbj.2025.06.019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In several fields, quantitatively comparing color images is crucial. For instance, this is important in Histopathology, where different microscopes/cameras are typically used for visualizing patient samples by causing significant color variation. No ground-truth metric exists for estimating differences between pairs of color images. A range of possible solutions is available but there is no existing open-source tool that allow clinicians and researchers to apply these metrics to microscopy images through an intuitive, easy-to-use software. In this work, we developed <i>Color Image Difference Tool</i> (<i>ColorI-DT</i>), an open-source tool for measuring quantitative differences between color images of the same subject acquired under different settings. Thanks to a user-friendly graphical user interface, it allows the selection of a pair of color images and a metric from a list of available options, and produces an output 2D pixel-wise color difference matrix between corresponding pixels in the input images. The metrics currently implemented are: (<i>1</i>) Euclidean <math><mrow><mi>Δ</mi> <mi>E</mi></mrow> </math> ; (<i>2</i>) International Commission on Illumination (CIE) 76 (Luv); (<i>3</i>) CIE76 (Lab); (<i>4</i>) CIE94; (<i>5</i>) CIE00; (<i>6</i>) Colour Measurement Committee (CMC). To demonstrate how to use the tool, microscopy images with a predominant color in the red, green, or blue channel were used. In particular, we checked which among the 6 metrics displays the most predictable and linear behavior in the case of controlled primary color alterations. For more pronounced color adjustments, a qualitative comparison would be likely sufficient for analyzing color differences, as a quantitative tool may become unreliable due to the inherent limitations of the implemented metrics.</p>\",\"PeriodicalId\":10715,\"journal\":{\"name\":\"Computational and structural biotechnology journal\",\"volume\":\"27 \",\"pages\":\"2526-2536\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12197881/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational and structural biotechnology journal\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1016/j.csbj.2025.06.019\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational and structural biotechnology journal","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.csbj.2025.06.019","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
ColorI-DT: An open-source tool for the quantitative evaluation of differences in microscopy color images.
In several fields, quantitatively comparing color images is crucial. For instance, this is important in Histopathology, where different microscopes/cameras are typically used for visualizing patient samples by causing significant color variation. No ground-truth metric exists for estimating differences between pairs of color images. A range of possible solutions is available but there is no existing open-source tool that allow clinicians and researchers to apply these metrics to microscopy images through an intuitive, easy-to-use software. In this work, we developed Color Image Difference Tool (ColorI-DT), an open-source tool for measuring quantitative differences between color images of the same subject acquired under different settings. Thanks to a user-friendly graphical user interface, it allows the selection of a pair of color images and a metric from a list of available options, and produces an output 2D pixel-wise color difference matrix between corresponding pixels in the input images. The metrics currently implemented are: (1) Euclidean ; (2) International Commission on Illumination (CIE) 76 (Luv); (3) CIE76 (Lab); (4) CIE94; (5) CIE00; (6) Colour Measurement Committee (CMC). To demonstrate how to use the tool, microscopy images with a predominant color in the red, green, or blue channel were used. In particular, we checked which among the 6 metrics displays the most predictable and linear behavior in the case of controlled primary color alterations. For more pronounced color adjustments, a qualitative comparison would be likely sufficient for analyzing color differences, as a quantitative tool may become unreliable due to the inherent limitations of the implemented metrics.
期刊介绍:
Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to:
Structure and function of proteins, nucleic acids and other macromolecules
Structure and function of multi-component complexes
Protein folding, processing and degradation
Enzymology
Computational and structural studies of plant systems
Microbial Informatics
Genomics
Proteomics
Metabolomics
Algorithms and Hypothesis in Bioinformatics
Mathematical and Theoretical Biology
Computational Chemistry and Drug Discovery
Microscopy and Molecular Imaging
Nanotechnology
Systems and Synthetic Biology