Medical Images Noise Removal using Improved Adversarial Generative Network
DOI:
https://doi.org/10.56294/mw2025838Keywords:
Computed Tomography, Magnetic Resonance, Noise Removal, Convolution Neural Networks, Adversarial Generative NetworkAbstract
Introduction; The protection of patient privacy through medical image de-identification stands as a vital yet complicated healthcare challenge which demands both accurate diagnosis and privacy protection. Deep learning techniques now provide superior methods to enhance medical images which suffer from acquisition noise and low-resolution degradation. The research develops a Generative Adversarial Network (GAN) architecture to create a deep-learning solution which enhances medical images while removing identifying information.
Objective; The proposed method uses adversarial learning to eliminate noise while restoring detailed high-resolution content from low-quality medical images.
Method; The research team analysed medical images through data analysis of their dataset. The GAN model received training and validation through experiments that compared its performance against established demo denoising and super-resolution techniques to assess its overall performance. The fundamental technology shows promise for future medical applications because it can enhance image quality to diagnose and treat multiple diseases. Medical image analysis requires images with diverse detailed features for proper evaluation.
Results; The proposed model demonstrates successful background noise reduction and improved image clarity with preserved diagnostic elements according to the obtained results. The proposed method achieved better results in PSNR and SSIM metrics than baseline models which proved its ability to restore vital diagnostic details.
Conclusion; The research introduces an innovative GAN-based system which delivers superior medical image quality while maintaining patient information confidentiality during de-identification processes. The method shows promise to create efficient and economical diagnostic processes through its ability to analyze poor-quality medical images.
References
1. Li Z. Imagine denoising methods based on GANs. Applied and Computational Engineering. 2024;106(1):25–31. doi:10.54254/2755-2721/106/20241263.
2. Ghadekar P, Gundawar A, Kamnapure S, Manjramkar D, Gujarathi I, Deore D. Improving image quality of noisy images through denoising and Style GAN technique. 2023:1–6. doi:10.1109/iccubea58933.2023.10392083.
3. S. V. Mohd Sagheer and S. N. George, "A review on medical image denoising algorithms", Biomed. Signal Process. Control, vol. 61, Aug. 2020.
4. M. Geng, X. Meng, J. Yu, L. Zhu, L. Jin, Z. Jiang, B. Qiu, H. Li, H. Kong, J. Yuan, K. Yang, H. Shan, H. Han, Z. Yang, Q. Ren, and Y. Lu, “Content-noise complementary learning for medical image denoising,” IEEE Transactions on Medical Imaging, vol. 41, no. 2, pp. 407–419, 2022.
5. R. Nair, S. Vishwakarma, M. Soni, T. Patel, and S. Joshi, “Detection of covid-19 cases through X-ray images using hybrid deep neural network,” World Journal of Engineering, vol. 19, no. 1, pp. 33–39, 2021.
6. R. Poonguzhali, S. Ahmad, P. T. Sivasankar et al., “Automated brain tumor diagnosis using deep residual u-net segmentation model,” Computers, Materials & Continua, vol. 74, no. 1, pp. 2179–2194, 2023.
7. D. L. Donoho, "Compressed sensing", IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
8. B. L. Y. Agbley, J. P. Li, A. U. Haq et al., “Federated Fusion of Magnified Histopathological Images for Breast Tumor Classification in the Internet of Medical Things,” in IEEE Journal of Biomedical and Health Informatics, 2023.
9. J. V. Manjón, J. Carbonell-Caballero, J. J. Lull, G. García-Martí, L. Martí-Bonmatí and M. Robles, "MRI denoising using non-local means", Med. Image Anal., vol. 12, no. 4, pp. 514-523, 2008.
10. P. F. Feruglio, C. Vinegoni, J. Gros, A. Sbarbati and R. Weissleder, "Block matching 3D random noise filtering for absorption optical projection tomography", Phys. Med. Biol., vol. 55, no. 18, pp. 5401, 2010.
11. A. M. Mendrik, E.-J. Vonken, A. Rutten, M. A. Viergever and B. van Ginneken, "Noise reduction in computed tomography scans using 3-D anisotropic hybrid diffusion with continuous switch", IEEE Trans. Med. Imag., vol. 28, no. 10, pp. 1585-1594, Oct. 2009.
12. O. Ronneberger, P. Fischer and T. Brox, "U-Net: Convolutional networks for biomedical image segmentation", Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent, pp. 234-241, 2015.
13. Y. Lu et al., "A learning-based material decomposition pipeline for multi-energy X-ray imaging", Med. Phys., vol. 46, no. 2, pp. 689-703, Feb. 2019.
14. M. Geng et al., "PMS-GAN: Parallel multi-stream generative adversarial network for multi-material decomposition in spectral computed tomography", IEEE Trans. Med. Imag., vol. 40, no. 2, pp. 571-584, Feb. 2021.
15. H. Chen et al., "Low-dose CT with a residual encoder-decoder convolutional neural network", IEEE Trans. Image Process., vol. 36, no. 12, pp. 2524-2535, Dec. 2017.
16. F. Fan et al., "Quadratic autoencoder (Q-AE) for low-dose CT denoising", IEEE Trans. Med. Imag., vol. 39, no. 6, pp. 2035-2050, Jun. 2020.
17. H. Shan et al., "3-D convolutional encoder-decoder network for low-dose CT via transfer learning from a 2-D trained network", IEEE Trans. Med. Imag., vol. 37, no. 6, pp. 1522-1534, Jun. 2018.
18. X. Yi and P. Babyn, "Sharpness-aware low-dose CT denoising using conditional generative adversarial network", J. Digit. Imag., vol. 31, no. 5, pp. 655-669, Oct. 2018.
19. R. Nair, D. K. Singh, Ashu, S. Yadav, and S. Bakshi, “Hand gesture recognition system for physically challenged people using IOT,” 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 2020.
20. R. Kashyap, R. Nair, S. M. Gangadharan, M. Botto-Tobar, S. Farooq, and A. Rizwan, “Glaucoma detection and classification using improved U-Net Deep Learning Model,” Healthcare, vol. 10, no. 12, p. 2497, 2022.
21. D. Lee, J. Yoo and J. C. Ye, "Deep artifact learning for compressed sensing and parallel MRI", arXiv:1703.01120, 2017, [online] Available: http://arxiv.org/abs/1703.01120.
22. Y. Han, J. Yoo, H. H. Kim, H. J. Shin, K. Sung and J. C. Ye, "Deep learning with domain adaptation for accelerated projection-reconstruction MR", Magn. Reson. Med., vol. 80, no. 3, pp. 1189-1205, Sep. 2018.
23. C. M. Hyun, H. P. Kim, S. M. Lee, S. Lee and J. K. Seo, "Deep learning for undersampled MRI reconstruction", Phys. Med. Biol., vol. 63, no. 13, Jun. 2018.
24. D. Jiang, W. Dou, L. Vosters, X. Xu, Y. Sun and T. Tan, "Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network", Jpn. J. Radiol., vol. 36, no. 9, pp. 566-574, Sep. 2018.
25. M. Kidoh et al., "Deep learning based noise reduction for brain mr imaging: Tests on phantoms and healthy volunteers", Magn. Reson. Med. Sci., vol. 19, no. 3, pp. 195, 2020.
26. L. Xiang et al., "Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI", Neurocomputing, vol. 267, pp. 406-416, Dec. 2017.
27. A. Sano, T. Nishio, T. Masuda and K. Karasawa, "Denoising PET images for proton therapy using a residual U-Net", Biomed. Phys. Eng. Exp., vol. 7, no. 2, Mar. 2021.
28. Y. Wang et al., "3D conditional generative adversarial networks for high-quality PET image estimation at low dose", NeuroImage, vol. 174, pp. 550-562, Jul. 2018.
29. . K. Zhang, W. Zuo, Y. Chen, D. Meng and L. Zhang, "Beyond a Gaussian Denoiser: Residual learning of deep CNN for image denoising", IEEE Trans. Image Process., vol. 26, no. 7, pp. 3142-3155, Jul. 2017.
30. T. Tong, G. Li, X. Liu and Q. Gao, "Image super-resolution using dense skip connections", Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp. 4799-4807, Oct. 2017.
31. P. Isola, J.-Y. Zhu, T. Zhou and A. A. Efros, "Image-to-image translation with conditional adversarial networks", Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 1125-1134, Jul. 2017.
32. H. Sun, L. Peng, H. Zhang, Y. He, S. Cao and L. Lu, "Dynamic PET image denoising using deep image prior combined with regularization by denoising", IEEE Access, vol. 9, pp. 52378-52392, 2021.
33. Haq AU, Li JP, Ahmad S, Khan S, Alshara MA, Alotaibi RM. Diagnostic approach for accurate diagnosis of COVID-19 employing deep learning and transfer learning techniques through chest X-ray images clinical data in E-healthcare. Sensors. 2021 Dec 9;21(24):8219
34. Alharbi, M., Ahmad, S. Enhancing COVID-19 detection using CT-scan image analysis and disease classification: the DI-QL approach. Health Technol. 15, 477–488 (2025). https://doi.org/10.1007/s12553-025-00952-0.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Ahmed A.F Osman, Asma Abdulmana Alhamadi, Sultan Ahmad, Rajit Nair, Mosleh Hmoud Al-Adhaileh, Ala Abdullah, Hikmat A. M. Abdeljaber, Mohammed Ataelfadiel (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
