Evidence map›Paper›PMID 39843646›Full record

ArticleScientific reports2025

An encryption algorithm for multiple medical images based on a novel chaotic system and an odd-even separation strategy.

Chunyun Xu, Yubao Shang, Yongwei Yang, Chengye Zou

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Medical Image Encryption Using Chaotic Mechanisms: A Study.Bioengineering (Basel, Switzerland) · 2025
    Review
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

4 authors.

Chunyun XuDepartment of Dermatology, Maternity and Child Health Hospital of Qinhuangdao, Qinhuangdao, 066000, China.
Yubao ShangSchool of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China.
Yongwei YangSchool of Mathematics and Computer Science, Tongling University, Tongling, 244000, China. yangyw2010@126.com.
Chengye ZouSchool of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China. zouchengye@ysu.edu.cn.

Funding

Key Scientific Research Program of Universities Funded by Henan Province 24B110002Post-doctoral Natural Science Foundation of China 2022M720650
6 · The paper itself

Abstract

The continuous evolution of information technology underscores the growing emphasis on data security. In the realm of medical imaging, various diagnostic images represent the privacy of individuals, and the potential repercussions of their unauthorized disclosure are substantial. Therefore, this study introduces a novel chaotic system (TLCMCML) and employs it to propose a multi-image medical image encryption algorithm. To simultaneously augment security and optimize encryption efficiency, we undertake a dual-pronged approach. Firstly, we identify the regions of interest (ROIs) within individual medical images, subsequently applying an independent scrambling technique based on an odd-even interleaving configuration. Secondly, we integrate all medical images through horizontal concatenation, forming a comprehensive large-scale image, upon which we implement a synchronized bit-level permutation-diffusion encryption mechanism. Following extensive testing, enhancements were verified across various metrics including information entropy analysis, adjacent pixel correlation examination, differential attack simulations, and robustness assessments, thereby attesting to the exceptional encryption efficacy of the proposed algorithm.

Indexed as

Medical imageMultiple image encryptionOdd-even separation strategyTLCMCML chaotic system

Identifiers

PMID39843646
PMCPMC11754641

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.