Quality Assessment of Color Normalization Method by Similarity Index Metrics- A Comparative Study for Histopathology Images

One of the biggest challenges of histopathology image processing is to preserve structural similarity while processing for further research.Color normalization algorithms can play a significant role in preserving the structure of histopathology images from various standpoints.In this research, we provide a comparative analysis of seven distinct color normalization algorithms by evaluating three state-of-the-art structural similarity index metrics often employed in image processing.100 malignant prostate cancer histopathology tissue images (256 256) from various grading (Gleason score 3, 4, and 5) have been utilized here.The structure similarity index matrix (SSIM), quaternion structure similarity index matrix (QSSIM), and multi-scale structure similarity index matrix (MS-SSIM) are three state-of-theart quality evaluation metrics used in this research.Also, by computing the mean standard deviation (SD) of the grayscale images to determine the noise level and signal-to-noise ratio (SNR), respectively, we examined six denoising algorithms with various parameters to improve the efficacy of this analysis.This study provides a higher value for each of the threesimilarity metrics, indicating a relatively superior performance for the Blind Color Decomposition algorithm.Furthermore, the Gaussian algorithm outperforms the six denoising techniques in terms of SNR and SD.When we integrated the Blind Color Decomposition and Gaussian algorithm with our experimented specific parameters, we were able to obtain the Rubina Akter Rabeya et al.: Quality Assessment of Color Normalization Method by Similarity Index Metrics-A Comparative Study for Histopathology Images ultimate higher value for all three structural similarity index metrics.We anticipate that this analysis will have a substantial impact on various aspects of histopathology image processing, including segmentation, classification, feature extraction, and the creation of novel algorithms.

Publication Details

Journal
KSII Transactions on Internet and Information Systems
Published
2025-05-31
DOI
https://doi.org/10.3837/tiis.2025.05.014
Citations
2
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
4.96

Funders

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article

Quality Assessment of Color Normalization Method by Similarity Index Metrics- A Comparative Study for Histopathology Images

2 citations
KSII Transactions on Internet and Information Systems
AI in cancer detection
4.96
article

Quality Assessment of Color Normalization Method by Similarity Index Metrics- A Comparative Study for Histopathology Images

article en
2 citations

Abstract

One of the biggest challenges of histopathology image processing is to preserve structural similarity while processing for further research.Color normalization algorithms can play a significant role in preserving the structure of histopathology images from various standpoints.In this research, we provide a comparative analysis of seven distinct color normalization algorithms by evaluating three state-of-the-art structural similarity index metrics often employed in image processing.100 malignant prostate cancer histopathology tissue images (256 256) from various grading (Gleason score 3, 4, and 5) have been utilized here.The structure similarity index matrix (SSIM), quaternion structure similarity index matrix (QSSIM), and multi-scale structure similarity index matrix (MS-SSIM) are three state-of-theart quality evaluation metrics used in this research.Also, by computing the mean standard deviation (SD) of the grayscale images to determine the noise level and signal-to-noise ratio (SNR), respectively, we examined six denoising algorithms with various parameters to improve the efficacy of this analysis.This study provides a higher value for each of the threesimilarity metrics, indicating a relatively superior performance for the Blind Color Decomposition algorithm.Furthermore, the Gaussian algorithm outperforms the six denoising techniques in terms of SNR and SD.When we integrated the Blind Color Decomposition and Gaussian algorithm with our experimented specific parameters, we were able to obtain the Rubina Akter Rabeya et al.: Quality Assessment of Color Normalization Method by Similarity Index Metrics-A Comparative Study for Histopathology Images ultimate higher value for all three structural similarity index metrics.We anticipate that this analysis will have a substantial impact on various aspects of histopathology image processing, including segmentation, classification, feature extraction, and the creation of novel algorithms.

KSII Transactions on Internet and Information SystemsVol. 19(5)
National Research Foundation, Korea Health Industry Development Institute, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion
Quality Education
Openalex Percentile: Top 5%
AI in cancer detection
4.96
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Quality Assessment of Color Normalization Method by Similarity Index Metrics- A Comparative Study for Histopathology Images · KSII Transactions on Internet and Information Systems (2025) | TGRS Research Map | TGRS