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<p>This study introduces a robust framework to address critical security requirements in digital healthcare systems, ensuring the confidentiality and integrity of medical data. It employs Laplacian redecomposition to fuse MRI and SPECT/PET images, creating a host image for embedding Aadhaar card image and a computed hash value. The embedding process integrates lifting wavelet transform, Hessenberg decomposition, and singular value decomposition to balance imperceptibility and robustness. A pseudo magic cube technique conceals the hash value, while an encryption scheme secures the watermarked image during transmission. Performance evaluations highlight strong results, with PSNR reaching 37.7895&#xa0;dB, SSIM up to 0.9993, and NC up to 0.9998, demonstrating resilience against various image processing attacks. This framework provides a reliable and effective solution for safeguarding medical data, addressing the pressing need for secure digital healthcare systems in an era of increasing reliance on telehealth and electronic health records.</p>

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Secure healthcare data management using multimodal image fusion and dual watermarking

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月17日

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  • Harendra Singh,
  • Maroti Deshmukh,
  • Lalit Kumar Awasthi

摘要

This study introduces a robust framework to address critical security requirements in digital healthcare systems, ensuring the confidentiality and integrity of medical data. It employs Laplacian redecomposition to fuse MRI and SPECT/PET images, creating a host image for embedding Aadhaar card image and a computed hash value. The embedding process integrates lifting wavelet transform, Hessenberg decomposition, and singular value decomposition to balance imperceptibility and robustness. A pseudo magic cube technique conceals the hash value, while an encryption scheme secures the watermarked image during transmission. Performance evaluations highlight strong results, with PSNR reaching 37.7895 dB, SSIM up to 0.9993, and NC up to 0.9998, demonstrating resilience against various image processing attacks. This framework provides a reliable and effective solution for safeguarding medical data, addressing the pressing need for secure digital healthcare systems in an era of increasing reliance on telehealth and electronic health records.