Evidence map›Paper›PMID 33323108›Full record

ArticleBMC medical informatics and decision making2020

A partial encryption algorithm for medical images based on quick response code and reversible data hiding technology.

Jian Li, Zelin Zhang, Shengyu Li, Ryan Benton, Yulong Huang, Mohan Vamsi Kasukurthi, Dongqi Li, Jingwei Lin, Glen M Borchert, Shaobo Tan and 4 more

Open access · goldAbstract read
In one paragraph

Article in BMC medical informatics and decision making, 2020. 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
1.6field-weighted citation impact, top 14% of its field
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, 22 citations in OpenAlex.

  1. Article
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  4. A simple embed over encryption scheme for DICOM images using Bülban Map.Medical & biological engineering & computing · 2022
    Article
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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

14 authors at 3 institutions in 2 countries.

Jian LiQilu University of Technology (Shandong Academy of Science), Shandong Provincial Key Laboratory of Computer Networks Jinan, Jinan, China.
Zelin ZhangQilu University of Technology (Shandong Academy of Science), Shandong Provincial Key Laboratory of Computer Networks Jinan, Jinan, China.
Shengyu LiSchool of Computing, University of South Alabama, Mobile, AL, 36688, USA.
Ryan BentonSchool of Computing, University of South Alabama, Mobile, AL, 36688, USA.
Yulong HuangCollege of Allied Health Professions, University of South Alabama, Mobile, AL, 36608, USA.
Mohan Vamsi KasukurthiSchool of Computing, University of South Alabama, Mobile, AL, 36688, USA.
Dongqi LiSchool of Computing, University of South Alabama, Mobile, AL, 36688, USA.
Jingwei LinOcean School, Fuzhou University, Fuzhou, China.
Glen M BorchertCollege of Medicine, University of South Alabama, Mobile, AL, 36688, USA.
Shaobo TanSchool of Computing, University of South Alabama, Mobile, AL, 36688, USA.
Gang LiQilu University of Technology (Shandong Academy of Science), Shandong Provincial Key Laboratory of Computer Networks Jinan, Jinan, China.
Bin MaQilu University of Technology (Shandong Academy of Science), Shandong Provincial Key Laboratory of Computer Networks Jinan, Jinan, China. mab@qlu.edu.cn.
Meihong YangQilu University of Technology (Shandong Academy of Science), Shandong Provincial Key Laboratory of Computer Networks Jinan, Jinan, China. yangmh@sdas.org.
Jingshan HuangSchool of Computing, University of South Alabama, Mobile, AL, 36688, USA. huang@southalabama.edu.
University of South Alabama · USQilu University of Technology · CNFuzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMedical image data, like most patient information, have a strong requirement for privacy and confidentiality. This makes transmitting medical image data, within an open network, problematic, due to the aforementioned issues, along with the dangers of data/information leakage. Possible solutions in the past have included the utilization of information-hiding and image-encryption technologies; however, these methods can cause difficulties when attempting to recover the original images.

methodsIn this work, we developed an algorithm for protecting medical image key regions. Coefficient of variation is first employed to identify key regions, a.k.a. image lesion areas; then additional areas are processed as blocks and texture complexity is analyzed. Next, our novel reversible data-hiding algorithm embeds lesion area contents into a high-texture area, after which an Arnold transformation is utilized to protect the original lesion information. After this, we use image basic information ciphertext and decryption parameters to generate a quick response (QR) code used in place of original key regions.

resultsThe approach presented here allows for the storage (and sending) of medical image data within open network environments, while ensuring only authorized personnel are able to recover sensitive patient information (both image and meta-data) without information loss. DISCUSSION: Peak signal to noise ratio and the Structural Similarity Index measures show that the algorithm presented in this work can encrypt and restore original images without information loss. Moreover, by adjusting the threshold and the Mean Squared Error, we can control the overall quality of the image: the higher the threshold, the better the quality and vice versa. This allows the encryptor to control the amount of degradation as, at appropriate amounts, degradation aids in the protection of the image.

conclusionsAs shown in the experimental results, the proposed method allows for (a) the safe transmission and storage of medical image data, (b) the full recovery (no information loss) of sensitive regions within the medical image following encryption, and (c) meta-data about the patient and image to be stored within and recovered from the public image.

Indexed as

AlgorithmsComputer SecurityConfidentialityHumansTechnologyImage segmentationKey regionQR codeReversible data hidingSelective encryptionTexture complexity

Identifiers

PMID33323108
PMCPMC7739464
OpenAlexW3112469008

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.