Improving Grayscale Medical Images (X-Rays, MRI, CT Scans)
DOI:
https://doi.org/10.70411/MJHAS.2.2.2025238Keywords:
Xـrays, MRI, CT scans, Medical images, Improve medical images, Medical image EnhancementAbstract
Medical images provide critical information that physicians need to diagnose a condition and make appropriate treatment decisions. The diagnostic process relies heavily on human perception. Unfortunately, the potential for perceptual errors is unacceptable, impacting patients' lives. Therefore, image enhancement represents a crucial step in supporting medical diagnosis by improving its quality. In this research, we applied a new algorithm to enhance X-ray, MRI, and CT images. This algorithm aims to improve contrast and sharpness based on an empirical equation using trigonometric functions (cos) based on Fourier transforms and changing the value of one of its main parameters. Nasal polyps were more clearly identified in X-rays, and brain tumours were more clearly identified in MRI and CT scans. Histograms were used before and after enhancement, and we observed a significant difference between the original and enhanced images in terms of the histogram shift toward unity, demonstrating the success of this algorithm in improving contrast and organ prominence.
References
Agrawal, U. K., Panda, N., Mohanty, P., Mahapatra, M., Verma, S., & Singh, D. (2025). Advancements in Healthcare Medical Imaging through SHO optimized CNN. Procedia Computer Science, 258, 4128-4135.
Akbar, M. U., Wang, W., & Eklund, A. (2025). Beware of diffusion models for synthesizing medical images—a comparison with GANs in terms of memorizing brain MRI and chest x-ray images. Machine Learning: Science and Technology, 6(1), 015022.
Al-Khafaji, K. H., & Sahib, H. A. (2024). Designing a Digital System for Enhancing, Coloring, Encryption and Decryption of X-ray Medical Image. 2024 15th International Conference on Information and Communication Systems (ICICS),
Anitha, N., Rajesh, T., Parwekar, P., & Chatradi, N. R. (2024). Denoising and Quality Enhancement of CT Scan/X-Ray Images of Lung Disease for Enhanced Diagnosis. Proceedings of Eighth International Conference on Information System Design and Intelligent Applications,
Babu, B. S., & Venkatanarayana, D. M. (2024). MRI and CT image fusion using cartoon-texture and QWT decomposition and cuckoo search-grey wolf optimization. Multimedia Tools and Applications, 83(3), 8797-8835.
Chen, Z., Pawar, K., Ekanayake, M., Pain, C., Zhong, S., & Egan, G. F. (2023). Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges. Journal of Digital Imaging, 36(1), 204-230.
Dremel, K., Prjamkov, D., Firsching, M., Weule, M., Lang, T., Papadaki, A., Kasperl, S., Blaimer, M., & Fuchs, T. O. (2025). Utilizing Quantum Annealing in Computed Tomography Image Reconstruction. IEEE Transactions on Quantum Engineering.
Falgout, D. M., Bevan, P. J., Grumet, R. C., & Parvaresh, K. C. (2024). Femoroacetabular impingement measurements obtained from two-dimensional radiographs versus three-dimensional–reconstructed computed tomography images result in different values. Arthroscopy, Sports Medicine, and Rehabilitation, 6(1), 100833.
Fan, J., Li, W., Zhou, Q., Yang, G., Tang, P., He, J., Ma, L., Zhang, J., Xiao, J., & Yan, Z. (2025). Metal Halide Perovskites for Direct X‐Ray Detection in Medical Imaging: To Higher Performance. Advanced Functional Materials, 35(21), 2401017.
Gómez-Guzmán, M. A., Jiménez-Beristaín, L., García-Guerrero, E. E., López-Bonilla, O. R., Tamayo-Perez, U. J., Esqueda-Elizondo, J. J., Palomino-Vizcaino, K., & Inzunza-González, E. (2023). Classifying brain tumors on magnetic resonance imaging by using convolutional neural networks. Electronics, 12(4), 955.
Hussain, S., Mubeen, I., Ullah, N., Shah, S. S. U. D., Khan, B. A., Zahoor, M., Ullah, R., Khan, F. A., & Sultan, M. A. (2022). Modern diagnostic imaging technique applications and risk factors in the medical field: a review. BioMed research international, 2022(1), 5164970.
Idowu, O., Amusa, K., & Ilori, A. (2022). Improved enhancement technique for medical image processing. Am. J. Eng. Res. 2022 Am. J. Eng. Res.(AJER(11), 126-137.
Kumari, P. (2025). Transforming Medical Imaging With Convolutional Neural Networks (CNNs): Advances in Diagnosis and Treatment. Deep Learning in Medical Signal and Image Processing, 195-230.
Li, Y., Iwamoto, Y., & Chen, Y.-W. (2019). Medical Image Enhancement Using Deep Learning. In Deep Learning in Healthcare: Paradigms and Applications (pp. 53-76). Springer.
Mouzai, M., Mustapha, A., Bousmina, Z., Keskas, I., & Farhi, F. (2023). Xray-Net: Self-supervised pixel stretching approach to improve low-contrast medical imaging. Computers and Electrical Engineering, 110, 108859.
Nayak, K. S., Lim, Y., Campbell‐Washburn, A. E., & Steeden, J. (2022). Real‐time magnetic resonance imaging. Journal of Magnetic Resonance Imaging, 55(1), 81-99.
Ou, X., Chen, X., Xu, X., Xie, L., Chen, X., Hong, Z., Bai, H., Liu, X., Chen, Q., & Li, L. (2021). Recent development in x-ray imaging technology: Future and challenges. Research.
Pinto-Coelho, L. (2023). How artificial intelligence is shaping medical imaging technology: a survey of innovations and applications. Bioengineering, 10(12), 1435.
Pourasad, Y., & Cavallaro, F. (2021). A novel image processing approach to enhancement and compression of X-ray images. International Journal of Environmental Research and Public Health, 18(13), 6724.
Reda, R., Zanza, A., Mazzoni, A., Cicconetti, A., Testarelli, L., & Di Nardo, D. (2021). An update of the possible applications of magnetic resonance imaging (MRI) in dentistry: a literature review. Journal of imaging, 7(5), 75.
Rehman, A., & Mir, S. Q. (2025). Introduction to Medical Image Segmentation: Overview of Modalities, Benchmark Datasets, Data Augmentation Techniques, and Evaluation Metrics. Deep Learning Applications in Medical Image Segmentation: Overview, Approaches, and Challenges, 1-26.
Stewart, G. D., Godoy, A., Farquhar, F., Kitt, J., Cartledge, J., Kimuli, M., Rossi, S. H., Shinkins, B., Burbidge, S., & Burge, S. W. (2025). Abdominal noncontrast computed tomography scanning to screen for kidney cancer and other abdominal pathology within community-based computed tomography screening for lung cancer: results of the yorkshire kidney screening trial. European Urology, 87(5), 561-570.
Sule, D. S., & Ampofo, R. (2025). Diagnostic Imaging Techniques: Current Trends and Challenges. Diagnostic Advances in Precision Medicine and Drug Development, 49-66.
Yadav, P., & Donvir, A. (2025). Enhancing Medical Imaging Accessibility: An AI-Powered Automated Tactile and 3D-Printable X-Ray System. 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC),
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