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Open AccessArticle

A New Algorithm for Medical Color Images Encryption Using Chaotic Systems

School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China
Authors to whom correspondence should be addressed.
Entropy 2019, 21(6), 577;
Received: 11 May 2019 / Revised: 1 June 2019 / Accepted: 4 June 2019 / Published: 10 June 2019
(This article belongs to the Special Issue Entropy in Image Analysis II)
In this paper, we present a new algorithm based on chaotic systems to protect medical images against attacks. The proposed algorithm has two main parts: A high-speed permutation process and adaptive diffusion. After the implementation of the algorithm in the MATLAB software, it is observed that the algorithm is effective and appropriate. Also, to quantitatively evaluate the uniformity of the histogram, the chi-square test is done. Key sensitivity analysis demonstrates that images cannot be decrypted whenever a small change happens in the key, which indicates that the algorithm is suitable. Clearly, part of special images is selected to test the selected plain-text, like an all-white image and an all-black image. Entropy results obtained from the implementation of the algorithm on this type of images show that the proposed method is suitable for this particular type of images. In addition, the obtained results from noise and occlusion attacks analysis show that the proposed algorithm can withstand against these types of attacks. Moreover, it can be seen that the images after encryption and decryption are of good quality; the measures such as the correlation coefficients, the entropy, the number of pixel change rate (NPCR), and the uniform average change intensity (UACI) have suitable values; and the method is better than previous methods. View Full-Text
Keywords: image encryption; medical color images; RGB; chaotic system image encryption; medical color images; RGB; chaotic system
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Moafimadani, S.S.; Chen, Y.; Tang, C. A New Algorithm for Medical Color Images Encryption Using Chaotic Systems. Entropy 2019, 21, 577.

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