Volume 18, Issue 4 (Paramedical Sciences and Military Health (winter 2024) 2024)
Paramedical Sciences and Military Health 2024, 18(4): 1-15 |
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Baharloo M, Samadzadehaghdam N. Quantitative Evaluation of Pseudo-Color Algorithms for Breast Thermography Images. Paramedical Sciences and Military Health 2024; 18 (4) :1-15
URL: http://jps.ajaums.ac.ir/article-1-394-en.html
URL: http://jps.ajaums.ac.ir/article-1-394-en.html
1- Department of Biomedical Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
2- Department of Biomedical Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran ,nsamadzadeh_a@yahoo.com
2- Department of Biomedical Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran ,
Abstract: (452 Views)
Introduction: Breast cancer is one of the most common diseases among women in all over the world. One of the effective ways to prevent the risk of death from breast cancer is early detection with thermographic breast screening methods. These images are sometimes provided as grayscale images. It is necessary to increase the quality of medical image for a better visual understanding. Pseudo coloring of thermal images provides physicians with more accurate diagnosis.
Methods and Materials: We evaluate the performance of eight pseudo-color algorithms in converting grayscale images to color images. These algorithms include warm color map (HCM), sinusoidal color map (SCM), proposed Zahedi color map (ZCM), jet color map (JCM), four algorithms proposed by Samadzadehaghdam that two of them are in HSI color space and the other two are in Lab color space. We compare the results with each other quantitatively. For evaluation, we use the measures of mean square error (MSE), normalized color difference (NCD), peak signal-to-noise ratio (PSNR), and structural similarity index metric (SSIM). Finally, we measure the significance of the differences with analysis of variance (ANOVA).
Results: The results show that the two algorithms designed in HSI color space are superior to other methods according to MSE, PSNR, and SSIM criteria.
Discussion and Conclusion: The human eye is much more sensitive to colors than gray levels. Therefore, converting grayscale thermograms to pseudo-colored ones results in better visualization, easy interpretation, and accurate diagnosis. Although there are various pseudo coloring methods, objective and quantitative metrics can help select the efficient algorithm.
Methods and Materials: We evaluate the performance of eight pseudo-color algorithms in converting grayscale images to color images. These algorithms include warm color map (HCM), sinusoidal color map (SCM), proposed Zahedi color map (ZCM), jet color map (JCM), four algorithms proposed by Samadzadehaghdam that two of them are in HSI color space and the other two are in Lab color space. We compare the results with each other quantitatively. For evaluation, we use the measures of mean square error (MSE), normalized color difference (NCD), peak signal-to-noise ratio (PSNR), and structural similarity index metric (SSIM). Finally, we measure the significance of the differences with analysis of variance (ANOVA).
Results: The results show that the two algorithms designed in HSI color space are superior to other methods according to MSE, PSNR, and SSIM criteria.
Discussion and Conclusion: The human eye is much more sensitive to colors than gray levels. Therefore, converting grayscale thermograms to pseudo-colored ones results in better visualization, easy interpretation, and accurate diagnosis. Although there are various pseudo coloring methods, objective and quantitative metrics can help select the efficient algorithm.
Type of Study: Research |
Subject:
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Received: 2024/01/13 | Accepted: 2024/04/17 | Published: 2024/08/20
Received: 2024/01/13 | Accepted: 2024/04/17 | Published: 2024/08/20
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