Evidence map›Paper›PMID 40659745›Full record

ArticleScientific reports2025

A novel 2D MTMHM based key generation for enhanced security in medical image communication.

C Sivaranjani Devi, Rengarajan Amirtharajan

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 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.

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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

2 authors.

C Sivaranjani DeviSchool of Electrical & Electronics Engineering, SASTRA Deemed University, Thirumalaisamudram, Thanjavur, 613401, India. dpfi0423021792@sastra.ac.in.
Rengarajan AmirtharajanSchool of Electrical & Electronics Engineering, SASTRA Deemed University, Thirumalaisamudram, Thanjavur, 613401, India.

Funding

DST FIST Fund SR/FST/ET-I/2018/221(C)
6 · The paper itself

Abstract

In today's tech-driven world, secure communication of medical information is a critical necessity. Protecting the patient's sensitive medical data through encryption algorithms based on chaos theory has emerged as a prominent research trend. This research proposes a novel 2D-Modified Tinkerbell Map with Henon Map (2D-MTMHM) chaotic equation to generate the pseudo-random key sequences for medical image encryption. Combining the Tinkerbell map with the Henon map exhibits a broader range of chaotic behaviour, making it highly suitable for cryptographic applications. The nature, randomness and sensitivity of the developed 2D-MTMHM equation are validated through the NIST SP800-22 statistical test, bifurcation diagram, Lyapunov exponent, permutation entropy, attractor trajectory, sample entropy and sensitivity test. The generated random key sequences trigger the proposed medical image encryption algorithm, which integrates a shuffling-diffusion process. The shuffling unit of the proposed medical image encryption scheme consists of three distinct phases: row-wise shuffling, column-wise shuffling, and selective shuffling based on cut-off points. The diffusion unit is designed to bit-wise scramble the pixel-shuffled image, further enhancing the randomness and security of the encrypted image. Simulation and experimental analysis demonstrate that the encryption system effectively resists statistical, differential and Brute-force attacks. The algorithm achieves an average entropy of 7.99, a correlation coefficient nearer to zero, a Number of Pixels Change Ratio (NPCR) of 99.6%, and a Unified Average Changing Intensity (UACI) of 33.4%. A larger key space of 10

Indexed as

2D-chaotic mapDICOMDiscrete mapImage encryptionSecurity analysisSelective shuffling

Identifiers

PMID40659745
PMCPMC12259849

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LicenceCC BY-NC-ND
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Registered trials

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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.