ColorI-DT:用于定量评估显微镜彩色图像差异的开源工具。

IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Computational and structural biotechnology journal Pub Date : 2025-06-09 eCollection Date: 2025-01-01 DOI:10.1016/j.csbj.2025.06.019
Filippo Piccinini, Michele Tritto, Jae-Chul Pyun, Misu Lee, Bongseop Kwak, Bosung Ku, Nicola Normanno, Gastone Castellani
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引用次数: 0

摘要

在一些领域,定量比较彩色图像是至关重要的。例如,这在组织病理学中很重要,在组织病理学中,不同的显微镜/相机通常用于通过引起显着的颜色变化来可视化患者样本。不存在用于估计彩色图像对之间差异的基础真值度量。一系列可能的解决方案是可用的,但没有现有的开源工具,使临床医生和研究人员能够通过直观,易于使用的软件将这些指标应用于显微镜图像。在这项工作中,我们开发了彩色图像差异工具(ColorI-DT),这是一个开源工具,用于测量在不同设置下获得的同一受试者的彩色图像之间的定量差异。由于用户友好的图形用户界面,它允许从可用选项列表中选择一对彩色图像和度量,并在输入图像中相应像素之间产生输出2D逐像素色差矩阵。目前实施的指标是:(1)欧几里得Δ E;(2)国际照明委员会76 (Luv);(3) CIE76(实验室);(4) CIE94;(5) CIE00;(6)颜色测量委员会(CMC)。为了演示如何使用该工具,使用了红色,绿色或蓝色通道中主要颜色的显微镜图像。特别地,我们检查了在控制原色改变的情况下,6个指标中哪一个显示出最可预测和线性的行为。对于更明显的颜色调整,定性比较可能足以分析颜色差异,因为定量工具可能由于实现度量的固有限制而变得不可靠。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 Δ E ; (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.

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来源期刊
Computational and structural biotechnology journal
Computational and structural biotechnology journal Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
9.30
自引率
3.30%
发文量
540
审稿时长
6 weeks
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
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