论文标题
混合混沌方法用于医学图像密封
Hybrid Chaotic Method for Medical Images Ciphering
论文作者
论文摘要
医疗保健是电子服务的重要应用，其中使用诊断测试，医学成像获取，处理，分析，存储和保护。在存储和传输过程中使用的网络传输期间的图像密码已看到使用多种类型的密码算法实现了安全目的。当前的密码算法分为两种类型：使用标准算法（DES，AES，IDEA，RC5，RSA，...）和使用连续（Chau，Chau，Rossler，Lorenz，...）或Choveet（Locisticals，Henon，Henon，...）Algorithms使用标准算法的传统经典加密术。与常规文本数据相比，传统算法一直在努力打击图像数据。而混沌算法对于图像密码更有效。混乱的重要性特征是其对初始条件和算法参数的极端敏感性。在本文中，提出了基于混合/混合混沌算法的医学图像安全性。提出的方法是使用MATLAB实现的。眼睛的视网膜图像检测血管。实施了来自不同混沌算法的伪随机数生成器（PRNG），并使用国家标准技术研究所NIST和其他统计测试套件对其统计属性进行评估。然后，这些算法用于保护数据，其中还测试了密码文本的统计属性。 We propose two PRNGs to increase the complexity of the PRNGs and to allow many of the NIST statistical tests to be passed: one based on two-hybrid mixed chaotic logistic maps and one based on two-hybrid mixed chaotic Henon maps, where each chaotic algorithm runs side-by-side and starts with random initial conditions and parameters (encryption keys).由此产生的混合动力PRNG通过了许多NIST统计测试诉讼。
Healthcare is an essential application of e-services, where for diagnostic testing, medical imaging acquiring, processing, analysis, storage, and protection are used. Image ciphering during storage and transmission over the networks used has seen implemented using many types of ciphering algorithms for security purpose. Current cyphering algorithms are classified into two types: traditional classical cryptography using standard algorithms (DES, AES, IDEA, RC5, RSA, ...) and chaos cryptography using continuous (Chau, Rossler, Lorenz, ...) or discreet (Logistics, Henon, ...) algorithms. The traditional algorithms have struggled to combat image data as compared to regular textual data. Whereas, the chaotic algorithms are more efficient for image ciphering. The Significance characteristics of chaos are its extreme sensitivity to initial conditions and algorithm parameters. In this paper, medical image security based on hybrid/mixed chaotic algorithms is proposed. The proposed method is implemented using MATLAB. Where the image of the Retina of the Eye to detect Blood Vessels is ciphered. The Pseudo-Random Numbers Generators (PRNGs) from the different chaotic algorithms are implemented, and their statistical properties are evaluated using the National Institute of Standards and Technology NIST and other statistical test-suits. Then, these algorithms are used to secure the data, where the statistical properties of the cipher-text are also tested. We propose two PRNGs to increase the complexity of the PRNGs and to allow many of the NIST statistical tests to be passed: one based on two-hybrid mixed chaotic logistic maps and one based on two-hybrid mixed chaotic Henon maps, where each chaotic algorithm runs side-by-side and starts with random initial conditions and parameters (encryption keys). The resulting hybrid PRNGs passed many of the NIST statistical test suits.
