Medical Image Encryption using Biometric Image Texture Fusion.

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...

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Publicado en:Journal of Medical Systems Vol. 47; no. 1; pp. 1 - 13
Autores principales: Liu, Zhaoyang, Xue, Ru
Formato: diagnostic images equations & formulas pictorial review tables/charts Journal Article
Publicado: Springer Nature 11/4/2023
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 11/4/2023
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-023-02003-5
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        atl: Medical Image Encryption using Biometric Image Texture Fusion.
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        au:
          Liu, Zhaoyang
          Xue, Ru
        affil: https://ror.org/042170a43 School of Information Engineering, Xizang Minzu University, 712082, Xianyang, Shaanxi, China
      sug:
        subj:
          Image Processing, Computer Assisted
          Encryption Methods
          Data Security Methods
          Biometrics Utilization
          Algorithms
          Information Storage
          Data Communications
          Computer Simulation
          Tomography, X-Ray Computed
          Magnetic Resonance Imaging
          X-Rays
          Ultrasonography
          Time Factors Evaluation
      ab: 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.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        pictorial
        review
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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