Comparative Analysis of RGB and Grayscale Pixel-Based Similarity Methods for Lung X-ray Image Retrieval in Clinical Decision Support Systems

Wahyu Wijaya Widiyanto; Mohd  Nizam Husen; Sofyan Pariyasto; Edy Susanto

doi:10.52436/1.jutif.2025.6.6.5570

Authors

Wahyu Wijaya Widiyanto Affiliation Health Information Management, Politeknik Indonusa Surakarta, Indonesia
Mohd Nizam Husen Malaysian Institute of Information Technology, Universiti Kuala Lumpur, Malaysia
Sofyan Pariyasto Medical Informatics, Sekolah Tinggi Ilmu Kesehatan Mitra Sejati, Medan, Indonesia
Edy Susanto Affiliation Health Information Management, Politeknik Indonusa Surakarta, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.6.5570

Keywords:

COVID-19, Grayscale Imaging, Lung X-ray, Medical Image Retrieval, Pixel-Based Similarity, RGB Comparison

Abstract

Chest X-ray imaging is widely used to support the diagnosis of lung diseases, yet many automated similarity techniques still rely on RGB formats, which differ from the grayscale images commonly used in clinical systems. This discrepancy raises the question of whether color information is necessary for effective similarity assessment. This study aims to evaluate the performance of RGB and grayscale pixel-based similarity methods for lung X-ray analysis and determine whether grayscale images can provide comparable similarity performance with lower computational demands. A total of 300 chest X-ray images representing normal, pneumonia, and COVID-19 categories were processed in both formats. Pixel-level similarity was calculated across 30,000 image pairings, followed by statistical testing to assess differences between methods. The results show that grayscale similarity scores closely match those of RGB, with variations generally below 0.3%. A meaningful difference was observed only in the comparison between normal and COVID-19 images, indicating that RGB may capture subtle visual variations not present in grayscale. Overall, this study demonstrates that grayscale pixel-based similarity analysis provides a reliable and computationally efficient approach, contributing to the development of lightweight medical image retrieval and clinical decision support systems in the field of health informatics.

Downloads

Download data is not yet available.

References

E. Çallı, E. Sogancioglu, B. van Ginneken, K. G. van Leeuwen, and K. Murphy, “Deep learning for chest X-ray analysis: A survey,” Med. Image Anal., vol. 72, p. 102125, Aug. 2021, doi: 10.1016/j.media.2021.102125.

E. Tiu, E. Talius, P. Patel, C. P. Langlotz, A. Y. Ng, and P. Rajpurkar, “Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning,” vol. 6, no. December, 2022, doi: 10.1038/s41551-022-00936-9.

P. G. Anderson et al., “Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays,” Sci. Rep., vol. 14, no. 1, p. 25151, 2024, doi: 10.1038/s41598-024-76608-2.

T. C. H. Hui et al., “Clinical utility of chest radiography for severe COVID-19,” vol. 10, no. 7, pp. 1540–1550, 2020, doi: 10.21037/qims-20-642.

M. S. Sho Maruyama, Haruyuki Watanabe, “An image quality assessment index based on image features and keypoints for X-ray CT images,” pp. 1–14, 2024, doi: 10.1371/journal.pone.0304860.

M. A. A. Adnane Ait Nasser, “A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography,” 2023.

M. S. Alhasan and A. S. Alhasan, “Technical requirements and optimization strategies for home-based teleradiology workstations: a review article,” Insights Imaging, vol. 16, no. 1, p. 198, Sep. 2025, doi: 10.1186/s13244-025-02081-8.

S. Dey et al., “Pixelator v2: A Novel Perceptual Image Comparison Method with LAB Colour Space and Sobel Edge Detection for Enhanced Security Analysis,” Electron., vol. 13, no. 22, pp. 1–22, 2024, doi: 10.3390/electronics13224541.

B. Kocak et al., “Evaluation metrics in medical imaging AI: fundamentals, pitfalls, misapplications, and recommendations,” vol. 3, no. June, 2025, doi: 10.1016/j.ejrai.2025.100030.

S. Pariyasto, V. A. Warongan, A. V. Sari, and W. W. Widiyanto, “Lung X-ray Image Similarity Analysis Using RGB Pixel Comparison Method,” vol. 9, no. 1, 2025.

B. Oltu, S. Güney, S. E. Yuksel, and B. Dengiz, “Automated classification of chest X-rays: a deep learning approach with attention mechanisms,” BMC Med. Imaging, vol. 25, no. 1, 2025, doi: 10.1186/s12880-025-01604-5.

G. Mehdiratta, J. T. Duda, A. Elahi, A. Borthakur, N. Chatterjee, and J. Gee, “Automated Integration of AI Results into Radiology Reports Using Common Data Elements Integrating the Healthcare Enterprise,” J. Imaging Informatics Med., vol. 38, no. 5, pp. 2623–2629, 2025, doi: 10.1007/s10278-025-01414-9.

D. W. M. Morteza Maali Amiri, “A new method for XRF and RGB image registration,” npj Herit. Sci., pp. 1–9, 2025, doi: 10.1038/s40494-025-01603-3.

W. C. Revie and P. Green, “Current problems and perspectives on colour in medical imaging,” IS T Int. Symp. Electron. Imaging Sci. Technol., pp. 1–5, 2016, doi: 10.2352/issn.2470-1173.2016.20.color-339.

D. Muhammad and M. Bendechache, “Unveiling the black box : A systematic review of Explainable Artificial Intelligence in medical image analysis,” Comput. Struct. Biotechnol. J., vol. 24, no. August, pp. 542–560, 2024, doi: 10.1016/j.csbj.2024.08.005.

S. A. Alowais et al., “Revolutionizing healthcare: the role of artificial intelligence in clinical practice,” BMC Med. Educ., vol. 23, no. 1, pp. 1–15, 2023, doi: 10.1186/s12909-023-04698-z.

Q. Llms et al., “Lightweight Clinical Decision Support System using,” pp. 1–12.

Z. Mahmood, “applied sciences Digital Image Processing : Advanced Technologies and Applications,” 2024.

A. Ghasemi and S. Zahediasl, “Normality Tests for Statistical Analysis: A Guide for Non-Statisticians Asghar,” vol. 10, no. 2, pp. 486–489, 2012, doi: 10.5812/ijem.3505.

W. Mckinney, “Data Structures for Statistical Computing in Python,” vol. 1, no. Scipy, pp. 56–61, 2010.