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Medical Image Encryption using Biometric Image Texture Fusion.

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Title: Medical Image Encryption using Biometric Image Texture Fusion.
Authors: Liu, Zhaoyang1,2,3, Xue, Ru1,2,3 rxue@xzmu.edu.cn
Source: Journal of Medical Systems. 11/4/2023, Vol. 47 Issue 1, p1-12. 12p.
Subjects: Digital image processing, Computer simulation, X-rays, Ultrasonic imaging, Time, Magnetic resonance imaging, Data security, Information retrieval, Data encryption, Biometry, Data transmission systems, Computed tomography, Algorithms
Abstract: In conjunction with pandemics, medical image data are growing exponentially. In some countries, hospitals collect biometric data from patients, such as fingerprints, iris, or faces. This data can be used for things like identity verification and security management. However, this medical data can be easily compromised by hackers. In order to prevent illegal tampering with medical images and invasion of privacy, a new texture fusion medical image encryption (TFMIE) algorithm derived from biometric images is proposed, which can encrypt the image using biometric information for storage or transmission. First, the medical image is decomposed into n-bit-planes by bit-plane decomposition. Secondly, a fusion image is generated by a biometric image with a circular local binary pattern and pixel-weighted average method. The fused image is further decomposed into n bit-planes through bit-plane decomposition and performs XOR operation with the original medical image in reverse order. Following the execution of the XOR operation, a new scrambling and diffusion algorithm based on a one-dimensional fractional trigonometric function (1DFTF) chaotic map is employed to form the cipher image. The experimental results show that compared with the existing methods, the average information entropy value of TFMIE is 7.99, and the average values of NPCR and UACI reach 0.9958 and 0.3346, respectively, which have strong key sensitivity, good robustness, and anti-attack ability. The method is lossless and has high transmission efficiency, which can meet the needs of medical big data encryption. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Medical Image Encryption using Biometric Image Texture Fusion.
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  Data: In conjunction with pandemics, medical image data are growing exponentially. In some countries, hospitals collect biometric data from patients, such as fingerprints, iris, or faces. This data can be used for things like identity verification and security management. However, this medical data can be easily compromised by hackers. In order to prevent illegal tampering with medical images and invasion of privacy, a new texture fusion medical image encryption (TFMIE) algorithm derived from biometric images is proposed, which can encrypt the image using biometric information for storage or transmission. First, the medical image is decomposed into n-bit-planes by bit-plane decomposition. Secondly, a fusion image is generated by a biometric image with a circular local binary pattern and pixel-weighted average method. The fused image is further decomposed into n bit-planes through bit-plane decomposition and performs XOR operation with the original medical image in reverse order. Following the execution of the XOR operation, a new scrambling and diffusion algorithm based on a one-dimensional fractional trigonometric function (1DFTF) chaotic map is employed to form the cipher image. The experimental results show that compared with the existing methods, the average information entropy value of TFMIE is 7.99, and the average values of NPCR and UACI reach 0.9958 and 0.3346, respectively, which have strong key sensitivity, good robustness, and anti-attack ability. The method is lossless and has high transmission efficiency, which can meet the needs of medical big data encryption. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10916-023-02003-5
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      – Code: eng
        Text: English
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        PageCount: 12
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      – SubjectFull: Digital image processing
        Type: general
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: X-rays
        Type: general
      – SubjectFull: Ultrasonic imaging
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      – SubjectFull: Time
        Type: general
      – SubjectFull: Magnetic resonance imaging
        Type: general
      – SubjectFull: Data security
        Type: general
      – SubjectFull: Information retrieval
        Type: general
      – SubjectFull: Data encryption
        Type: general
      – SubjectFull: Biometry
        Type: general
      – SubjectFull: Data transmission systems
        Type: general
      – SubjectFull: Computed tomography
        Type: general
      – SubjectFull: Algorithms
        Type: general
    Titles:
      – TitleFull: Medical Image Encryption using Biometric Image Texture Fusion.
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            NameFull: Liu, Zhaoyang
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            NameFull: Xue, Ru
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            – D: 04
              M: 11
              Text: 11/4/2023
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              Y: 2023
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