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Related Experiment Video

Updated: Nov 9, 2025

Immunostaining of Whole-Mount Retinas with the CLARITY Tissue Clearing Method

Immunostaining of Whole-Mount Retinas with the CLARITY Tissue Clearing Method

Published on: March 6, 2021

Retinex model based stain normalization technique for whole slide image analysis.

Md Ziaul Hoque¹, Anja Keskinarkaus¹, Pia Nyberg²

¹Physiological Signal Analysis Group, Center for Machine Vision and Signal Analysis, University of Oulu, Finland; Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|April 16, 2021

Summary

This study introduces a novel Retinex model for stain normalization in histopathology images, improving quantitative analysis and tissue segmentation. The method enhances accuracy and consistency for computer-aided diagnosis in biobanking.

Keywords:

Color normalization Computer aided diagnosis Illumination estimation Medical image analysis Stain separation

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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

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Last Updated: Nov 9, 2025

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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Area of Science:

Digital pathology
Histopathology image analysis
Computational imaging

Background:

Stained histological slides present challenges for quantitative analysis due to color and intensity variations.
Stain variability complicates automated tissue segmentation and whole slide imaging analysis.
Accurate stain normalization is crucial for reliable histopathological image quantification.

Purpose of the Study:

To develop and validate a Retinex model-based stain normalization technique for histopathological images.
To address variability in staining for improved tissue area segmentation and quantitative analysis.
To enhance the accuracy and reproducibility of computer-aided diagnosis in biobanking.

Main Methods:

Development of a Retinex model for stain normalization and area segmentation.
Quantification of individual stain components to remove staining variability.
Experimental comparison against reference methods using organotypic carcinoma and myoma tissue models.

Main Results:

The proposed method demonstrated the smallest standard deviation, skewness, and coefficient of variation in normalized median intensity measurements.
Achieved superior performance in QSSIM, SSIM, and PCC, indicating improved robustness and reproducibility.
Consistently outperformed reference methods in quantitative analysis of histopathological images.

Conclusions:

The Retinex model-based stain normalization effectively reduces variability in histopathological images.
The method offers enhanced accuracy and consistency for computer-aided diagnosis.
Potential applications include development of novel research and diagnostic tools in biobanking.