Evidence map›Paper›PMID 37622044›Full record

ArticleJournal of medical signals and sensors

A Highly Robust Medical Image Watermarking Method for Medical Real-time Applications.

Mahdi Mehrabi, Vahdi Zarei, Mohammad Ghanbari

Abstract read
In one paragraph

Article in Journal of medical signals and sensors. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Mahdi MehrabiDepartment of Computer Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
Vahdi ZareiDepartment of Computer Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
Mohammad GhanbariSchool of Computer Science and Electronic Engineering University of Essex Colchester, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Watermarking such as other security concepts is an ongoing challenging research issue, especially for medical images, to protect patient privacy. Medical images need to be shared and transferred between hospitals and specialists as quickly as possible for better diagnosis. Fast and simple watermarking is needed as well as the robust transferring of channel noise, such as salt and pepper noise and robust cropping that may occur from specialists and signature encryption for patient privacy. Methods: In this article, a highly robust and simple watermarking method is introduced. The proposed method has very low computational complexity and at the same time, it is very robust to interference and uses simple computations such as (XORs) Exclusive ORs and rotations that can be done in real-time. The proposed method uses a combination of hidden neighboring signature information, Sudoku permutation, and noise pre-processing to achieve high robustness against salt and pepper noise and cropping. Simple signature encryption is also used. Results: The proposed method is examined in different medical image datasets. The experimental results indicate the proposed watermarking system is robust to salt and pepper noise density of up to 90% and about 70% cropping. The number of computations including encryption is five XOR per pixel and a rotation per block of signature size. Conclusion: A novel method for medical image watermarking is presented. The proposed method is in the spatial domain, has encryption, and uses only XOR computation. The proposed method is highly robust to noise and cropping which is necessary for medical uses. The proposed method can be used efficiently for real-time watermarking for medical and nonmedical image datasets.

Indexed as

Image crop noisemedical image watermarkingreal-time watermarkingrobust watermarkingsalt and pepper noise

Identifiers

PMID37622044
PMCPMC10445676

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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.