Comparison of color normalization techniques for reduction of tissue heterogeneity in breast cancer histopathological images

Authors

DOI:

https://doi.org/10.24054/rcta.v1i47.4283

Keywords:

color normalization, histopathologic imaging, breast cancer

Abstract

Color normalization in histopathological images is considered an initial step aimed at contributing to consistency in the computational analysis of hematoxylin and eosin (H&E)-stained tissues. In this work, three color normalization methods are evaluated in histopathological images of breast cancer (Macenko, Reinhard, and Vahadane) using quantitative metrics ORB, SSIM, and histogram correlation. In terms of ORB and SSIM, Vahadane stood out for better preserving morphological structures with consistently high values, which is crucial for computational analysis. Nevertheless, Vahadane presented variability in histogram correlation, in contrast to Macenko and Reinhard, who achieved higher and consistent correlations across all samples with average values of 0.9628 and 0.9385. This highlights that the choice of method depends on the balance between structural fidelity and chromatic distribution depending on the case.

Downloads

Download data is not yet available.

References

C. Kaushal, S. Bhat, D. Koundal, and A. Singla, “Recent Trends in Computer Assisted Diagnosis (CAD) System for Breast Cancer Diagnosis Using Histopathological Images,” IRBM, vol. 40, no. 4, pp. 211–227, Aug. 2019, doi: 10.1016/j.irbm.2019.06.001.

S. yang Tang, L. Li, Y. lin Li, A. yuan Liu, M. jun Yu, and Y. ping Wan, “Distribution and location of Daxx in cervical epithelial cells with high risk human papillomavirus positive,” Diagn Pathol, vol. 9, no. 1, Jan. 2014, doi: 10.1186/1746-1596-9-1.

F. Li, J. Ma, T. Wen, Z. Tian, and H. N. Liang, “HI-Net: A novel histopathologic image segmentation model for metastatic breast cancer via lightweight dataset construction,” Heliyon, vol. 10, no. 19, p. e38410, Oct. 2024, doi: 10.1016/J.HELIYON.2024.E38410.

A. Agrawal and V. Maan, “Enhanced Brain Tumor Segmentation and Size Estimation in MRI Samples using Hybrid Optimization,” Data and Metadata, vol. 3, pp. 408–408, Jan. 2024, doi: 10.56294/DM2024408.

G. A. Ansari, S. S. Bhat, M. D. Ansari, S. Ahmad, and H. A. M. Abdeljaber, “Prediction and Diagnosis of Breast Cancer using Machine Learning Techniques,” Data and Metadata, vol. 3, p. .346-.346, Sep. 2024, doi: 10.56294/DM2024.346.

T. A. Azevedo Tosta, P. R. de Faria, L. A. Neves, and M. Z. do Nascimento, “Computational normalization of H&E-stained histological images: Progress, challenges and future potential,” Artif Intell Med, vol. 95, pp. 118–132, Apr. 2019, doi: 10.1016/J.ARTMED.2018.10.004.

K. de Haan et al., “Deep learning-based transformation of H&E stained tissues into special stains,” Nat Commun, vol. 12, no. 1, Dec. 2021, doi: 10.1038/s41467-021-25221-2.

M. Runz, D. Rusche, S. Schmidt, M. R. Weihrauch, J. Hesser, and C. A. Weis, “Normalization of HE-stained histological images using cycle consistent generative adversarial networks,” Diagn Pathol, vol. 16, no. 1, Dec. 2021, doi: 10.1186/s13000-021-01126-y.

B. Zhao et al., “RestainNet: A self-supervised digital re-stainer for stain normalization,” Computers and Electrical Engineering, vol. 103, Oct. 2022, doi: 10.1016/j.compeleceng.2022.108304.

C. Franchet et al., “Bias reduction using combined stain normalization and augmentation for AI-based classification of histological images,” Comput Biol Med, vol. 171, Mar. 2024, doi: 10.1016/j.compbiomed.2024.108130.

T. A. A. Tosta, A. D. Freitas, P. R. de Faria, L. A. Neves, A. S. Martins, and M. Z. do Nascimento, “A stain color normalization with robust dictionary learning for breast cancer histological images processing,” Biomed Signal Process Control, vol. 85, Aug. 2023, doi: 10.1016/j.bspc.2023.104978.

J. Jeong, K. D. Kim, Y. Nam, C. E. Cho, H. Go, and N. Kim, “Stain normalization using score-based diffusion model through stain separation and overlapped moving window patch strategies,” Comput Biol Med, vol. 152, Jan. 2023, doi: 10.1016/j.compbiomed.2022.106335.

Z. Tabatabaei, F. Pérez Bueno, A. Colomer, J. O. Moll, R. Molina, and V. Naranjo, “Advancing Content-Based Histopathological Image Retrieval Pre-Processing: A Comparative Analysis of the Effects of Color Normalization Techniques,” Applied Sciences (Switzerland), vol. 14, no. 5, Mar. 2024, doi: 10.3390/app14052063.

E. Bütün, M. Uçan, and M. Kaya, “Automatic detection of cancer metastasis in lymph node using deep learning,” Biomed Signal Process Control, vol. 82, p. 104564, Apr. 2023, doi: 10.1016/J.BSPC.2022.104564.

M. Veta, P. J. van Diest, R. Kornegoor, A. Huisman, M. A. Viergever, and J. P. W. Pluim, “Automatic Nuclei Segmentation in H&E Stained Breast Cancer Histopathology Images,” PLoS One, vol. 8, no. 7, p. e70221, Jul. 2013, doi: 10.1371/JOURNAL.PONE.0070221.

M. Macenko et al., “A method for normalizing histology slides for quantitative analysis,” Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 1107–1110, 2009, doi: 10.1109/ISBI.2009.5193250.

E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley, “Color transfer between images,” IEEE Comput Graph Appl, vol. 21, no. 5, pp. 34–41, Sep. 2001, doi: 10.1109/38.946629.

A. Vahadane et al., “Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images,” IEEE Trans Med Imaging, vol. 35, no. 8, pp. 1962–1971, Aug. 2016, doi: 10.1109/TMI.2016.2529665.

A. Sethi et al., “Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images,” J Pathol Inform, vol. 7, no. 1, p. 17, Jan. 2016, doi: 10.4103/2153-3539.179984.

S. Roy, S. Panda, and M. Jangid, “Modified Reinhard Algorithm for Color Normalization of Colorectal Cancer Histopathology Images,” 2021 29th European Signal Processing Conference (EUSIPCO), pp. 1231–1235, Aug. 2021, doi: 10.23919/EUSIPCO54536.2021.9616117.

Published

2026-01-01

How to Cite

[1]
“Comparison of color normalization techniques for reduction of tissue heterogeneity in breast cancer histopathological images”, RCTA, vol. 1, no. 47, pp. 26–33, Jan. 2026, doi: 10.24054/rcta.v1i47.4283.

Similar Articles

1-10 of 611

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)