A review of research on medical image confidentiality related technology coherent taxonomy, motivations, open challenges and recommendations

Zhang, Bin; Rahmatullah, Bahbibi; Wang, Shir Li; Zaidan, A. A.; Zaidan, B. B.; Liu, Penghui

doi:10.1007/s11042-020-09629-4

Cited by 17 publications

(17 citation statements)

References 135 publications

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Section: Introductionmentioning

confidence: 99%

“…With the rapid development of information technology and medical imaging technology, the number of medical images is increasing at an alarming rate [1][2][3]. Many medical imaging systems generate and store medical images in different ways, such as ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography, and other techniques [1,2,4]. With the wide application of wireless communication systems, especially for the fifth-generation (5G) network, the update speed of the Internet of things (IoT) technology is getting faster and faster [5], and the application of wearable IoT sensors to track patients' vital signs information is increasing day by day [6].…”

Section: Introductionmentioning

confidence: 99%

“…e medical images may be leaked and tampered with, which will endanger the lives and property of patients [1]. Due to these growing threats, the protection of digital medical images is becoming more and more important [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust and Secure Zero-Watermarking Algorithm for Medical Images Based on Harris-SURF-DCT and Chaotic Map

Gong

Bhatti

et al. 2021

Security and Communication Networks

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To protect the patient information in medical images, this article proposes a robust watermarking algorithm for medical images based on Harris-SURF-DCT. First, the corners of the medical image are extracted using the Harris corner detection algorithm, and then, the previously extracted corners are described using the method of describing feature points in the SURF algorithm to generate the feature descriptor matrix. Then, the feature descriptor matrix is processed through the perceptual hash algorithm to obtain the feature vector of the medical image, which is a binary feature vector with a size of 32 bits. Secondly, to enhance the security of the watermark information, the logistic map algorithm is used to encrypt the watermark before embedding the watermark. Finally, with the help of cryptography knowledge, third party, and zero-watermarking technology, the algorithm can embed the watermark without modifying the medical image. When extracting the watermark, the algorithm can extract the watermark from the test image without the original image. In addition, the algorithm has strong robustness to conventional attacks and geometric attacks. Especially under geometric attacks, the algorithm performs better.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust and Secure Zero-Watermarking Algorithm for Medical Images Based on Harris-SURF-DCT and Chaotic Map

Gong

Bhatti

et al. 2021

Security and Communication Networks

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“…Scopus covers the world's most extensive scientific and medical publications in abstracts, references, and indexes, which collects publications from many famous journals. 24 …”

Section: Methodsmentioning

confidence: 99%

A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis

Zhang

Rahmatullah

Wang

et al. 2021

J Applied Clin Med Phys

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Medical images are important in diagnosing disease and treatment planning.Computer algorithms that describe anatomical structures that highlight regions of interest and remove unnecessary information are collectively known as medical image segmentation algorithms. The quality of these algorithms will directly affect the performance of the following processing steps. There are many studies about the algorithms of medical image segmentation and their applications, but none involved a bibliometric of medical image segmentation. Methods: This bibliometric work investigated the academic publication trends in medical image segmentation technology. These data were collected from the Web of Science (WoS) Core Collection and the Scopus. In the quantitative analysis stage, important visual maps were produced to show publication trends from five different perspectives including annual publications, countries, top authors, publication sources, and keywords. In the qualitative analysis stage, the frequently used methods and research trends in the medical image segmentation field were analyzed from 49 publications with the top annual citation rates. Results: The analysis results showed that the number of publications had increased rapidly by year. The top related countries include the Chinese mainland, the United States, and India. Most of these publications were conference papers, besides there are also some top journals. The research hotspot in this field was deep learning-based medical image segmentation algorithms based on keyword analysis. These publications were divided into three categories: reviews, segmentation algorithm publications, and other relevant publications. Among these three categories, segmentation algorithm publications occupied the vast majority, and deep learning neural network-based algorithm was the research hotspots and frontiers. Conclusions: Through this bibliometric research work, the research hotspot in the medical image segmentation field is uncovered and can point to future research in the field. It can be expected that more researchers will focus their work on deep learning neural network-based medical image segmentation.

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“…In general, we use time complexity and space complexity to refer to runtime and space requirements; when complexity is used without qualifier, it usually refers to time complexity [55]. The time complexity analysis of an encryption algorithm is an important index of encryption performance, and the computational complexity is used to analyse the computer resources needed to run the algorithm.…”

Section: J Complexity Analysis Of Encryption Algorithmmentioning

confidence: 99%

A Novel Chaotic Block Image Encryption Algorithm Based on Deep Convolutional Generative Adversarial Networks

Fang

Liu

2021

IEEE Access

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This paper proposes a novel chaotic block image encryption algorithm based on deep convolutional generative adversarial networks (DCGANs), quaternions, an improved Feistel network, and an overall scrambling and diffusion mechanism. First, a new hyperchaotic system is introduced and combined with DCGANs to generate a random sequence with better randomness and complexity as a key stream. This sequence is then combined with a quaternion and an improved Feistel network encryption of a colour plaintext image by utilizing the key block matrix to ultimately achieve overall scrambling and diffusion of the cipher image. Finally, the security of this algorithm is quantitatively and qualitatively analysed. The simulation results show that the proposed hyperchaotic system has a large key space and good random characteristics and that the new algorithm yields adequate security and can resist brute-force attacks and chosen-plaintext attacks. Therefore, this approach provides a new way to achieve secure transmission and protection of image information. INDEX TERMS Image encryption, chaotic system, deep convolutional generative adversarial networks.

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A review of research on medical image confidentiality related technology coherent taxonomy, motivations, open challenges and recommendations

Cited by 17 publications

References 135 publications

Robust and Secure Zero-Watermarking Algorithm for Medical Images Based on Harris-SURF-DCT and Chaotic Map

Robust and Secure Zero-Watermarking Algorithm for Medical Images Based on Harris-SURF-DCT and Chaotic Map

A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis

A Novel Chaotic Block Image Encryption Algorithm Based on Deep Convolutional Generative Adversarial Networks

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