Security, confidentiality, and privacy of information in the field of health with data EPR embedding in medical MRI images based on HVS model
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Jalil Zabihi 1, Hadi Grailo2 |
1- Faculty of Electrical and Robotics Engineering, Shahroud University of Technology, Semnan, Iran , zabihi85@gmail.com 2- Faculty of Electrical and Robotics Engineering, Shahroud University of Technology, Semnan, Iran |
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Abstract: (3709 Views) |
Introduction: Patient data security has become a serious concern. One of the methods against these risks is the utilization of data embedding. The patient’s electronic record is embedded into the medical image without changing the shape and size of the image.
Methods: The EPR data embedding is based on SSIM and HVS criteria employing MRI image segmentation. The idea behind two-dimensional wavelet-cosine conversion based on EPR data latency is that the final image will provide more resilience and stability against malicious attacks.
Results: The present study aimed to study the effect of the proposed method, including innovations in the HVS block based on SSIM criteria for transparency and flexibility. The latency coefficient K is considered to be comparatively proportional to the N-ROI uniformity. The selection of coefficients includes the frequency and spatial sensitivity optimization function in an adaptive way to match the visual perception of vision.
Conclusion: Security in health information systems has its own weak points, planning to secure access to patients and risk management is crucial , and necessary. Research improve the level of security, confidentiality, and integrated storage of patient information and image. The simulation results, considering the parameters of robustness and transparency in comparison with other methods, were carried out with the evaluation criteria, including MSE, NC, SSIM, and BER measurements. |
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Keywords: Data security, Evaluation, Data embedding, Electronic patient record, MS-SSIM, Human visual system model |
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Full-Text [PDF 1230 kb]
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Type of Article: Research Article |
Subject:
Health Management Received: 2021/09/18 | Revised: 2022/02/9 | Accepted: 2022/01/30 | ePublished: 2022/02/7
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