Evidence map›Paper›PMID 33267291›Full record

ArticleEntropy (Basel, Switzerland)2019

A New Algorithm for Medical Color Images Encryption Using Chaotic Systems.

Seyed Shahabeddin Moafimadani, Yucheng Chen, Chunming Tang

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Medical Image Encryption Using Chaotic Mechanisms: A Study.Bioengineering (Basel, Switzerland) · 2025
    Review
  3. Entropy in Image Analysis II.Entropy (Basel, Switzerland) · 2020
    Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Seyed Shahabeddin MoafimadaniSchool of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China.
Yucheng ChenSchool of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China.
Chunming TangSchool of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China.

Funding

Collaborative Innovation Major Projects of Bureau of Education of Guangzhou City 1201610005Foundation of National Natural Science of China 61772147Guangdong Province Natural Science Foundation of major basic research and Cultivation project 2015A030308016National Cryptography Development Fund MMJJ20170117Project of Ordinary University Innovation Team Construction of Guangdong Province 2015KCXTD014
6 · The paper itself

Abstract

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.

Indexed as

chaotic systemimage encryptionmedical color imagesRGB

Identifiers

PMID33267291
PMCPMC7515066

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