Robust Medical Image Watermarking and Brain TumorSegmentation Using Multi-Domain Transforms And Deep ‎Learning

  • Authors

    • S Sujatha Department of Electronics and Communication Engineering, SV University College of Engineering, Tirupati, India
    • T Sreenivasulu Reddy Department of Electronics and Communication Engineering, SV University College of Engineering, Tirupati, India
    https://doi.org/10.14419/7zws5x66

    Received date: June 16, 2025

    Accepted date: July 18, 2025

    Published date: July 28, 2025

  • Medical Image Watermarking; Brain Tumor Segmentation; DWT-DCT-SVD; Hybrid U-Net; Black Widow ‎Optimization; Genetic Algorithm; Thingspeak; Image Security‎.

  • Abstract

    This observation introduces a unified framework that guarantees both the stable watermarking of medical pics and ‎effective brain tumor segmentation. By leveraging a hybrid approach that integrates deep learning with a ‎couple of area transformation strategies, the device complements photograph security and diagnostic precision. ‎The approach embeds MRI scans into host photographs through a fusion of Discrete Wavelet Transform, Discrete ‎Cosine Transform, and Singular Value Decomposition. The embedding strength is adaptively optimized using ‎Black Widow Optimization guided by a Genetic Algorithm, enhancing robustness and imperceptibility. DWT ‎decomposes the images, DCT shifts them to the frequency domain, and SVD modifies singular values with an ‎adaptive power factor. Inverse operations reconstruct the watermarked image, authenticated using a unique ‎code via the ThingSpeak IoT platform. Successful authentication enables watermark extraction through ‎inverse SVD, DCT, and DWT, yielding high fidelity (PSNR > 34 dB, SSIM > 0.91). A Hybrid U-Net-based deep ‎mastering segmentation method is employed to extract mind tumors from watermarked clinical snapshots for ‎diagnostic analysis. Morphological operations and boundary extraction refine the segmented regions. Tumor ‎stages—initial, middle, or advanced—are determined based on pixel count. This dual-purpose framework ‎ensures secure embedding and reliable transmission of sensitive medical data, while also providing accurate and ‎efficient brain tumor detection. The system is highly relevant for applications in telemedicine, secure medical ‎data sharing, and remote diagnosis‎.

  • References

    1. Hamidi, M.; El Haziti, M.; Cherifi, H.; El Hassouni, M. “A Hybrid Robust Image Watermarking Method Based on DWT-DCT and SIFT for Copy-right Protection”. J. Imaging 2021, 7, 218. https://doi.org/10.3390/jimaging7100218.
    2. J. Liu et al., "An Optimized Image Watermarking Method Based on HD and SVD in DWT Domain," in IEEE Access, vol. 7, pp. 80849-80860, 2019, https://doi.org/10.1109/ACCESS.2019.2915596.
    3. Vahideh Hayyolalam, Ali Asghar Pourhaji Kazem, “Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineer-ing optimization problems”, Engineering Applications of Artificial Intelligence, Volume 87, 2020, 103249, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2019.103249.
    4. Sohail Saif, Priya Das, Suparna Biswas, Shakir Khan, Mohd Anul Haq, Viacheslav Kovtun, “A secure data transmission framework for IoT enabled healthcare”, Heliyon, Volume 10, Issue 16, 2024, e36269, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2024.e36269.
    5. B. Bawankar, B. Manjre and V. P. Uplanchiwar, "IOT Based Patient Health Monitoring System Using ThingSpeak," 2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS), Nagpur, India, 2022, pp. 378-382. https://doi.org/10.1109/ICETEMS56252.2022.10093488.
    6. Sara, U., Akter, M. and Uddin, M. (2019), “Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study”. Journal of Computer and Communications, 7, 8-18. https://doi.org/10.4236/jcc.2019.73002.
    7. Ronneberger, O., Fischer, P., Brox, T. (2015). “U-Net: Convolutional Networks for Biomedical Image Segmentation”. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_28.
    8. Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation”. Nat Methods 18, 203–211 (2021). https://doi.org/10.1038/s41592-020-01008-z.
    9. Alwan, A.H., Ali, S.A., Hashim, A.T. (2024). “Medical image segmentation using enhanced residual U-Net architecture”. Mathematical Modelling of Engineering Problems, Vol. 11, No. 2, pp. 507-516. https://doi.org/10.18280/mmep.110223.
    10. Rezvani, S., Fateh, M., & Khosravi, H. (2024). “ABANet: Attention boundary-aware network for image segmentation”. Expert Systems, 41(9), e13625. https://doi.org/10.1111/exsy.13625.
    11. Narima Zermi, Amine Khaldi, Redouane Kafi, Fares Kahlessenane, Salah Euschi, “A DWT-SVD based robust digital watermarking for medical image security”, Forensic Science International, Volume 320, 2021, 110691, ISSN 0379-0738, https://doi.org/10.1016/j.forsciint.2021.110691.
    12. A. K. Katkoori and R. Boda, “A Threshold-based Brain Tumour Segmentation from MR Images using Multi-Objective Particle Swarm Optimiza-tion,” Journal of Information Systems and Telecommunication (JIST), vol. 9, no. 4, pp. 218–225, Oct.–Dec. 2021. https://doi.org/10.52547/jist.9.36.218.
    13. L. Chandrashekar and Sreedevi A, "A Two-Stage Multi-Objective Enhancement for Fused Magnetic Resonance Image and Computed Tomography Brain Images," Journal of Information Systems and Telecommunication (JIST), vol. 8, no. 2, pp. 93–104, Apr.–Jun. 2020. https://doi.org/10.29252/jist.8.30.93.
    14. A. Sandooghdar and F. Yaghmaee, "Deep Learning Approach for Cardiac MRI Images," Journal of Information Systems and Telecommunication (JIST), vol. 10, no. 1, pp. 61–67, Jan.–Mar. 2022. https://doi.org/10.52547/jist.16121.10.37.61.
    15. Harun-Ar-Rashid, M.; Chowdhury, O.; Hossain, M.M.; Rahman, M.M.; Muhammad, G.; AlQahtani, S.A.; Alrashoud, M.; Yassine, A.; Hossain, M.S. “IoT-Based Medical Image Monitoring System Using HL7 in a Hospital Database”. Healthcare 2023, 11, 139. https://doi.org/10.3390/healthcare11010139.
    16. Jason Walsh, Alice Othmani, Mayank Jain, Soumyabrata Dev, “Using U-Net network for efficient brain tumor segmentation in MRI images, Healthcare Analytics”, Volume 2, 2022, 100098, ISSN 2772-4425, https://doi.org/10.1016/j.health.2022.100098.
    17. M., Pravin Savaridass & Deepika, Rada & Aarnika, R & Maniraj, V & Gokilanandhi, P & Kowsika, K. (2021). “Digital Watermarking For Medical Images Using Dwt And Svd Technique”. IOP Conference Series: Materials Science and Engineering. 1084. 012034. https://doi.org/10.1088/1757-899X/1084/1/012034.
    18. S. Sujatha and T. S. Reddy, "3D Brain Tumor Segmentation with U-Net Network using Public Kaggle Dataset," 2023 Third International Confer-ence on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, 2023, pp. 829-835. https://doi.org/10.1109/ICAIS56108.2023.10073895.

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  • How to Cite

    Sujatha, S. ., & Reddy, T. S. . (2025). Robust Medical Image Watermarking and Brain TumorSegmentation Using Multi-Domain Transforms And Deep ‎Learning. International Journal of Basic and Applied Sciences, 14(3), 406-417. https://doi.org/10.14419/7zws5x66