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相关实验视频

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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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使用生物识别图像纹理融合的医疗图像加密.

Zhaoyang Liu1,2,3, Ru Xue4,5,6

  • 1School of Information Engineering, Xizang Minzu University, 712082, Xianyang, Shaanxi, China.

Journal of medical systems
|November 4, 2023
PubMed
概括

一种新的纹理融合医疗图像加密 (TFMIE) 算法使用生物识别数据来保护敏感的医疗图像. 这种方法为医疗大数据提供了强大的保护,防止改和侵犯隐私.

关键词:
位平面分解比特平面分解混乱的地图 混乱的地图医疗图像加密 医疗图像加密纹理的融合 纹理的融合

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科学领域:

  • 医疗成像医学成像
  • 密码学 密码学 密码学 密码学
  • 生物识别信息 生物识别信息

背景情况:

  • 医学图像数据的指数式增长,特别是在流行病期间,需要加强安全措施.
  • 医院收集的生物识别数据 (指纹,虹膜,面部) 容易受到黑客攻击和隐私侵犯.
  • 现有的加密方法可能无法充分保护敏感的医疗图像和患者隐私.

研究的目的:

  • 提出一种使用生物识别信息的新纹理融合医疗图像加密 (TFMIE) 算法.
  • 防止在存储或传输期间非法改和侵犯医疗图像的隐私.
  • 提高医疗大数据加密的安全性和效率.

主要方法:

  • 医疗图像被分解成n位平面.
  • 使用生物识别数据,循环局部二进制模式和像素加权平均值创建一个融合图像.
  • 融合的图像经历了比特平面分解和XOR操作与原始医疗图像.
  • 一个一维的分数三角函数 (1DFTF) 混乱地图被用于混和扩散.

主要成果:

  • 该TFMIE算法实现了7.99.9的平均信息.
  • NPCR (像素数变化速率) 和UACI (统一平均变化强度) 的平均值分别达到0.9958和0.3346.
  • 与现有方法相比,表现出强大的关键灵敏度,稳定性和反攻击能力.
  • 加密过程无损,传输效率高.

结论:

  • 拟议的TFMIE算法有效地使用生物识别信息加密医疗图像,确保数据安全和隐私.
  • 该方法的高性能指标 (,NPCR,UACI) 表明其适用于保护医疗大数据.
  • 对于医疗图像加密日益增长的需求,TFMIE提供了一个强大,高效和无损的解决方案.