ColorI-DT: An open-source tool for the quantitative evaluation of differences in microscopy color images.
Filippo Piccinini1,2, Michele Tritto3, Jae-Chul Pyun4
1IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Via Piero Maroncelli 40, Meldola, FC 47014, Italy.
Computational and Structural Biotechnology Journal
|June 27, 2025
Summary
This study introduces Color Image Difference Tool (ColorI-DT), an open-source software for quantitatively comparing color microscopy images. It helps researchers and clinicians assess color variations from different imaging settings using various color difference metrics.
Area of Science:
- Digital Image Analysis
- Computational Imaging
- Color Science
Background:
- Quantitative comparison of color images is essential in fields like histopathology, where variations arise from different microscopes and cameras.
- Existing methods lack a standardized, open-source tool for applying color difference metrics to microscopy images.
- No ground-truth metric currently exists for accurately estimating differences between color image pairs.
Purpose of the Study:
- To develop an open-source software tool, Color Image Difference Tool (ColorI-DT), for quantitative comparison of color images.
- To provide an intuitive graphical user interface for applying various color difference metrics to microscopy images.
- To evaluate the performance of implemented color difference metrics on controlled color alterations.
Main Methods:
- Developed Color Image Difference Tool (ColorI-DT) with a user-friendly graphical interface.
- Implemented six quantitative color difference metrics: Euclidean ΔE, CIE 76 (Luv), CIE 76 (Lab), CIE94, CIE00, and CMC.
- Utilized microscopy images with controlled primary color alterations to test metric behavior.
Main Results:
- ColorI-DT generates a 2D pixel-wise color difference matrix between corresponding pixels of input images.
- Evaluation of implemented metrics showed varying degrees of predictable and linear behavior with controlled color changes.
- The study identified specific metrics that perform more predictably under certain color alteration conditions.
Conclusions:
- ColorI-DT offers a valuable open-source solution for quantitative color image comparison in research and clinical settings.
- The choice of color difference metric impacts the reliability and linearity of quantitative results, especially with pronounced color shifts.
- While quantitative tools are useful, qualitative assessment may be sufficient for significant color variations due to metric limitations.
