Region-Based Hybrid Medical Image Watermarking Scheme for Robust and Secured Transmission in IoMT

Abstract

With the growth in Internet and digital technology, Internet of Medical Things (IoMT) and Telemedicine have become buzzwords in healthcare. A large number of medical images and information is shared through a public network in these applications. This paper proposes a region-based hybrid Medical Image Watermarking (MIW) scheme to ensure the authenticity, authorization, integrity, and confidentiality of the medical images transmitted through a public network in IoMT. In the proposed scheme, medical image is segmented into Region of Interest (RoI) and Region of Non-Interest (RoNI). RoI tamper detection and recovery bits are embedded in RoI to ensure the integrity of the medical image. RoI is watermarked using adaptive Least Significant Bit (LSB) substitution with respect to the hiding capacity map for higher RoI imperceptibility and accuracy in tamper detection and recovery. Electronic Patient Record (EPR) is compressed using Huffman coding and encrypted using a pseudo-random key (secret key) to provide higher confidentiality and payload. Encrypted EPR, QR code of hospital logo, and RoI recovery bits are embedded in RoNI using Discrete Wavelet Transform-Singular Value Decomposition (DWT-SVD) hybrid transforms to achieve a robust watermark. The proposed scheme is tested under various geometric and non-geometric attacks such as filtering, compression, rotation, salt and pepper noise and shearing. The evaluation results demonstrate that the proposed scheme has high imperceptibility, robustness, security, payload, tamper detection, and recovery accuracy under image processing attacks. Therefore, the proposed scheme can be used in the transmission of medical images and EPR in IoMT. Relevance of the proposed scheme is established by its superior performance in comparison to some of the popular existing schemes.

Multimodal Fusion-Based Image Hiding Algorithm for Secure Healthcare System

“…For example, when Gaussian noise with density = 0.01, the NC score of the proposed algorithm is 0.9792 greater than that of [11] and [20]. However, when Sharpening noise with density = 0.1, the NC score of the proposed algorithm is greater than [19] and [20]. Further, comparative analysis of the proposed algorithm with some existing works [10], [11], [19], [20] in terms of some more parameters is carried out in Table 4.…”

“…3. Comparative analysis of the proposed algorithm with some existing works [11], [19], [20] is carried out in Table 3. The NC scores of the proposed algorithm are higher than the schemes in [11], [19], [20] under considered attacks.…”

“…Comparative analysis of the proposed algorithm with some existing works [11], [19], [20] is carried out in Table 3. The NC scores of the proposed algorithm are higher than the schemes in [11], [19], [20] under considered attacks. For example, when Gaussian noise with density = 0.01, the NC score of the proposed algorithm is 0.9792 greater than that of [11] and [20].…”

“…However, when Sharpening noise with density = 0.1, the NC score of the proposed algorithm is greater than [19] and [20]. Further, comparative analysis of the proposed algorithm with some existing works [10], [11], [19], [20] in terms of some more parameters is carried out in Table 4. For example, when the payload =0.2500 bit/pixel, PSNR and SSIM score of the proposed algorithm is 63.98 dB and 1.00, respectively, greater than that of [10], [11], [19] and [20].…”

“…Further, comparative analysis of the proposed algorithm with some existing works [10], [11], [19], [20] in terms of some more parameters is carried out in Table 4. For example, when the payload =0.2500 bit/pixel, PSNR and SSIM score of the proposed algorithm is 63.98 dB and 1.00, respectively, greater than that of [10], [11], [19] and [20]. Furthermore, the NC score of the proposed algorithm is '1' equal to [11] and [20], and the NC score of our algorithm is greater than [10] and [19].…”