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Journal ArticleDOI

Security of medical images for telemedicine: a systematic review

TL;DR: In this article , the authors present a detailed discussion of different types of medical images and the attacks that may affect medical image transmission and present an in-depth overview of security techniques, such as cryptography, steganography, and watermarking.
Abstract: Recently, there has been a rapid growth in the utilization of medical images in telemedicine applications. The authors in this paper presented a detailed discussion of different types of medical images and the attacks that may affect medical image transmission. This survey paper summarizes existing medical data security approaches and the different challenges associated with them. An in-depth overview of security techniques, such as cryptography, steganography, and watermarking are introduced with a full survey of recent research. The objective of the paper is to summarize and assess the different algorithms of each approach based on different parameters such as PSNR, MSE, BER, and NC.
Citations
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Journal ArticleDOI
27 Jul 2022-Sensors
TL;DR: This paper derived multi-channel Gaussian–Hermite moments of fractional-order (MFrGHMs) and used a kernel-based method for the highly accurate computation of MFr GHMs to solve the computation issue and constructed image features that are accurate and robust.
Abstract: Copyright protection of medical images is a vital goal in the era of smart healthcare systems. In recent telemedicine applications, medical images are sensed using medical imaging devices and transmitted to remote places for screening by physicians and specialists. During their transmission, the medical images could be tampered with by intruders. Traditional watermarking methods embed the information in the host images to protect the copyright of medical images. The embedding destroys the original image and cannot be applied efficiently to images used in medicine that require high integrity. Robust zero-watermarking methods are preferable over other watermarking algorithms in medical image security due to their outstanding performance. Most existing methods are presented based on moments and moment invariants, which have become a prominent method for zero-watermarking due to their favorable image description capabilities and geometric invariance. Although moment-based zero-watermarking can be an effective approach to image copyright protection, several present approaches cannot effectively resist geometric attacks, and others have a low resistance to large-scale attacks. Besides these issues, most of these algorithms rely on traditional moment computation, which suffers from numerical error accumulation, leading to numerical instabilities, and time consumption and affecting the performance of these moment-based zero-watermarking techniques. In this paper, we derived multi-channel Gaussian–Hermite moments of fractional-order (MFrGHMs) to solve the problems. Then we used a kernel-based method for the highly accurate computation of MFrGHMs to solve the computation issue. Then, we constructed image features that are accurate and robust. Finally, we presented a new zero-watermarking scheme for color medical images using accurate MFrGHMs and 1D Chebyshev chaotic features to achieve lossless copyright protection of the color medical images. We performed experiments where their outcomes ensure the robustness of the proposed zero-watermarking algorithms against various attacks. The proposed zero-watermarking algorithm achieves a good balance between robustness and imperceptibility. Compared with similar existing algorithms, the proposed algorithm has superior robustness, security, and time computation.

6 citations

Journal ArticleDOI
TL;DR: In this paper , a hybrid machine learning approach was used to classify breast cancer images in a novel manner with the help of a newly devised algorithm that is conceptually more sound as compared to already existing algorithms.
Abstract: Medical imaging is the process of visualizing the diseased part, inside the patient’s body, with the aid of images. The field of medical imaging depends on several disciplines of science and technology, including physics, biological sciences, engineering, artificial intelligence and mathematics. These disciplines contribute in designing the imaging devices, installation of the devices and the collection and analysis of the images for better understanding and future forecasting of the disease prognosis and prevention. In this manuscript, medical images are analyzed with the aid of the a new hybrid machine learning approach, where the breast cancer images are studied in a novel manner with the help of a newly devised algorithm that is conceptually more sound as compared to already existing algorithms. Step by step stages are followed by the algorithm to process, filter, segment, statistically analyze and to classify the medical images. The results from different classification tools are compared in a novel manner, inspired from the explainable artificial intelligence tools for classification. The algorithm devised during this research can serve as a useful tool, in the evolving field of particle - physics -imaging.

5 citations

Journal ArticleDOI
TL;DR: The decrypted elemental image array (EIA) can be reconstructed into a full-color and full-parallax 3D image using the display device, which can be visually displayed to doctors so that they can observe from different angles to design accurate treatment plans and improve the level of medical treatment.
Abstract: This Letter proposes a selective encryption scheme for three-dimensional (3D) medical images using light-field imaging and two-dimensional (2D) Moore cellular automata (MCA). We first utilize convolutional neural networks (CNNs) to obtain the saliency of each elemental image (EI) originating from a 3D medical image with different viewpoints, and successfully extract the region of interest (ROI) in each EI. In addition, we use 2D MCA with balanced rule to encrypt the ROI of each EI. Finally, the decrypted elemental image array (EIA) can be reconstructed into a full-color and full-parallax 3D image using the display device, which can be visually displayed to doctors so that they can observe from different angles to design accurate treatment plans and improve the level of medical treatment. Our work also requires no preprocessing of 3D images, which is more efficient than the method of using slices for encryption.

2 citations

References
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Journal ArticleDOI
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

40,609 citations

Journal ArticleDOI
TL;DR: An effective technique for image authentication which can prevent malicious manipulations but allow JPEG lossy compression, and describes adaptive methods with probabilistic guarantee to handle distortions introduced by various acceptable manipulations such as integer rounding, image filtering, image enhancement, or scaling-recaling.
Abstract: Image authentication verifies the originality of an image by detecting malicious manipulations. Its goal is different from that of image watermarking, which embeds into the image a signature surviving most manipulations. Most existing methods for image authentication treat all types of manipulation equally (i.e., as unacceptable). However, some practical applications demand techniques that can distinguish acceptable manipulations (e.g., compression) from malicious ones. In this paper, we present an effective technique for image authentication which can prevent malicious manipulations but allow JPEG lossy compression. The authentication signature is based on the invariance of the relationships between discrete cosine transform (DCT) coefficients at the same position in separate blocks of an image. These relationships are preserved when DCT coefficients are quantized in JPEG compression. Our proposed method can distinguish malicious manipulations from JPEG lossy compression regardless of the compression ratio or the number of compression iterations. We describe adaptive methods with probabilistic guarantee to handle distortions introduced by various acceptable manipulations such as integer rounding, image filtering, image enhancement, or scaling-recaling. We also present theoretical and experimental results to demonstrate the effectiveness of the technique.

618 citations

Book ChapterDOI
20 Oct 2003
TL;DR: An information-theoretic method for performing steganography and steganalysis using a statistical model of the cover medium is presented, which achieves a higher embedding efficiency and message capacity than previous methods while remaining secure against first order statistical attacks.
Abstract: This paper presents an information-theoretic method for performing steganography and steganalysis using a statistical model of the cover medium The methodology is general, and can be applied to virtually any type of media It provides answers for some fundamental questions which have not been fully addressed by previous steganographic methods, such as how large a message can be hidden without risking detection by certain statistical methods, and how to achieve this maximum capacity Current steganographic methods have been shown to be insecure against fairly simple statistical attacks Using the model-based methodology, an example steganography method is proposed for JPEG images which achieves a higher embedding efficiency and message capacity than previous methods while remaining secure against first order statistical attacks

470 citations

Journal ArticleDOI
TL;DR: Simulations and performance evaluations show that the proposed system is able to produce a one-dimension (1D) chaotic system with better chaotic performances and larger chaotic ranges compared with the previous chaotic maps.

458 citations

Journal ArticleDOI
TL;DR: For every clever method and tool being developed to hide information in multimedia data, an equal number of clever methods and tools areBeing developed to detect and reveal its secrets.
Abstract: For every clever method and tool being developed to hide information in multimedia data, an equal number of clever methods and tools are being developed to detect and reveal its secrets.

444 citations