Piccinini Filippo, Tritto Michele, Pyun Jae-Chul, Lee Misu, Kwak Bongseop, Ku Bosung, Normanno Nicola, Castellani Gastone
IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Via Piero Maroncelli 40, Meldola, FC 47014, Italy.
Department of Medical and Surgical Sciences (DIMEC), University of Bologna, via G. Massarenti 9, Bologna 40138, Italy.
Comput Struct Biotechnol J. 2025 Jun 9;27:2526-2536. doi: 10.1016/j.csbj.2025.06.019. eCollection 2025.
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 (), 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: () Euclidean ; () International Commission on Illumination (CIE) 76 (Luv); () CIE76 (Lab); () CIE94; () CIE00; () 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.
在多个领域，对彩色图像进行定量比较至关重要。例如，在组织病理学中这一点很重要，在该领域中，不同的显微镜/相机通常用于通过造成显著的颜色变化来可视化患者样本。不存在用于估计成对彩色图像之间差异的真实度量标准。有一系列可能的解决方案，但没有现有的开源工具允许临床医生和研究人员通过直观、易于使用的软件将这些度量标准应用于显微镜图像。在这项工作中，我们开发了（），这是一个开源工具，用于测量在不同设置下获取的同一受试者的彩色图像之间的定量差异。由于其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一种度量标准，并生成输入图像中对应像素之间的二维逐像素颜色差异矩阵。目前实现的度量标准有：（）欧几里得距离；（）国际照明委员会（CIE）76（Luv）；（）CIE76（Lab）；（）CIE94；（）CIE00；（）颜色测量委员会（CMC）。为了演示如何使用该工具，我们使用了在红色、绿色或蓝色通道中具有主导颜色的显微镜图像。特别是，我们检查了在控制原色变化的情况下，这6种度量标准中哪一种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，因为由于所实现度量标准的固有局限性，定量工具可能变得不可靠。
