Medical image security authentication method based on wavelet reconstruction and fractal dimension

Sun, Tiankai; Wang, Xingyuan; Lin, Defu; Bao, Rong; Jiang, Daihong; Ding, Bin; Li, Dan

doi:10.1177/15501477211014132

Cited by 7 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another frequency domain-based watermarking system for medical image authentication is proposed in [17], where the wavelet transform and fractal dimension theory are used. The regularity of the mutation structure in the medical image is examined using a measure specified in the frequency domain.…”

Section: Literature Reviewmentioning

confidence: 99%

A Secure Self-Embedding Technique for Manipulation Detection and Correction of Medical Images

Tareef

2024

IJCDS

View full text Add to dashboard Cite

The protection of medical images transmitted through the E-healthcare system is very critical. Nowadays, medical image watermarking has been emerged as trustworthy way to authenticate medical information during transmission. This paper presents a secure self-embedding scheme that detects and corrects the tampesr in medical images. The proposed scheme involved two decomposition and dimensionality reduction techniques, singular value decomposition and learning sparse decomposition. First, the color medical image is transformed into YCrCb color space and the luminance plane is chosen. To create the watermark, the medical image is automatically classified into region of interest (ROI) and region of non-interest (RONI), and then, the ROI is encoded by sparse decomposition with convolutional Basis Pursuit DeNoising (BPDN) dictionary. The sparse watermark is then hidden in the singular values of the host part of the image. The quantitative and qualitative results show that the proposed method is robust against numerous aggressive and geometric distortions without compromising the quality of the original medical image. The proposed algorithm yields a high Peak Signal-to-Noise Ratio (PSNR) larger than 45dB for all type of images, as well as high normalized correlation (NC) value under all types of attacks. It is demonstrated that the presented system performs better than the existing techniques, and could be helpful for e-healthcare systems.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Secure Self-Embedding Technique for Manipulation Detection and Correction of Medical Images

Tareef

2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…Various steps were composed, such as conversion of a frequency domain, image encryption and key generation using optimized HCM, and the decryption of an image. In 2021, Sun et al [1] archived the medical image authentication by combining fractal dimension theory and the multi-scale wavelet transform.…”

Section: Related Workmentioning

confidence: 99%

“…Every small difference or change in medical data can cause medical controversies and disputes. That's why through the authentication process, no noise is added and only using the internal characteristics and inherent properties of the digital medical data information complete and finish copyright authentication to become an appropriate scheme for medical information authentication [1].…”

Section: Introductionmentioning

confidence: 99%

Medical Image Encryption Based on Frequency Domain and Chaotic Map

Yousif¹,

Mahdi²,

Hashim³

2022

IJSSE

View full text Add to dashboard Cite

Medical images typically have diagnostic and confidential data and information about the patient and are usually sent using public networks. Due to the sensitivity of medical images, their security has become a challenging requirement that must be addressed. Traditional cryptographical algorithms are inadequate to ensure appropriate and fine security while encrypting them, because of the correlation between each pixel, high redundancy of the image and its major size. Chaotic systems with their properties and partial encryption based on frequency domain are the best candidates for securing the storage and transfer of digital images. The paper shows new criteria for encryption of medical image. It is designed to improve performance and meet the increasing need for better security for medical image encryption. At first, the pixel's correlation is eliminated by scrambling the input image and diffusing them using secret sharing based on polynomials. Various frequency domains of the image are accomplished by applying the integer wavelet transform of the scrambled image, namely, the associated detail of (HL, LH and HH) and the LL (approximation coefficient) through using the AES algorithm. The (LL) part is encrypted to originate the diffusion image and by using the inverse of the Haar wavelet transform to produce a reliable, unbreakable and secure form. The designed algorithm is used to reverse and shuffle every frequency sign of the transformed image before transformation image back to the pixel domain. The original image form is restored through the reverse decryption algorithm. The suggested algorithm is measured and evaluated in a statistical way and normal standard security; The outcome of the proposed algorithm shows a strong resistant to the familiar attacks and extra secure than the other algorithms in the domain of image cryptography.

show abstract

“…Medical images play a vital role in modern medical diagnosis and treatment. With the continuous development of medical imaging technology, various medical image data, such as radiology images, medical ultrasound images and pathological images, are constantly emerging, which provide doctors with rich information and help them make accurate diagnosis and treatment plans [1][2]. However, because the acquisition process of medical images is limited by equipment and technology, images may be affected by noise, artifacts, insufficient resolution and other problems, which reduces the quality and usability of medical images.…”

Section: Introductionmentioning

confidence: 99%

Research and Application of Deep Learning in Medical Image Reconstruction and Enhancement

Gong,

Qiu,

Liu

et al. 2024

FCIS

View full text Add to dashboard Cite

In recent years, deep learning technology has made remarkable progress in medical image reconstruction and enhancement, and has become one of the research hotspots in the field of medical image processing. This paper discusses the latest research progress and application of deep learning in medical image reconstruction and enhancement. Firstly, the importance of medical image reconstruction and enhancement and the limitations of traditional methods are introduced. Then, a detailed discussion was conducted on the application of deep learning models, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Autoencoders, in medical image processing. Specifically, an analysis and comparison were conducted on the image reconstruction ability of CNN models, the image enhancement effect of GAN models, and the image denoising and reconstruction of Autoencoder models. Then, the advantages and challenges of deep learning model in medical image processing are discussed, and the future development direction is discussed. Finally, the research results of this paper are summarized and the prospect of future research is put forward. The research in this paper provides some enlightenment and reference for researchers and practitioners in the field of medical image processing, which is helpful to promote the continuous innovation and progress of medical image processing technology.

show abstract

Medical image security authentication method based on wavelet reconstruction and fractal dimension

Cited by 7 publications

References 8 publications

A Secure Self-Embedding Technique for Manipulation Detection and Correction of Medical Images

A Secure Self-Embedding Technique for Manipulation Detection and Correction of Medical Images

Medical Image Encryption Based on Frequency Domain and Chaotic Map

Research and Application of Deep Learning in Medical Image Reconstruction and Enhancement

Contact Info

Product

Resources

About