Medical Image Protection Research Articles

Robust Zero-Watermarking of Color Medical Images Using Multi-Channel Gaussian-Hermite Moments and 1D Chebyshev Chaotic Map.

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.

A New Data Model for the Privacy Protection of Medical Images

Benefiting from the intelligent Medical Internet of Things (IoMT), the medical industry has dramatically improved its quality and productivity. The transmission of biomedical data in an open and untrusted network poses a new challenge to the privacy protection of patient information. The low processing power of IoMT limited the application of traditional encryption to protect sensitive data. In this paper, we developed a new data protection model for medical images. The model uses visual cryptography (VC) to store biomedical data in a separate database, which can transfer the sensitive data of patients simply and securely. To alleviate the degradation of biomedical recognition performance caused by VC-based noise, we further use transfer learning to train an optimized neural network. The experimental results show that this proposed method provides privacy in the IoMT environment and maintains the high accuracy of biomedical recognition.

Secure and efficient privacy protection system for medical records

The healthcare sector has witnessed robust growth in the realm of medical information and its application during the last two decades. Consequently, the universal availability of patient records using web-based technology has acquired great significance in providing quality healthcare services on a global scale. With paramount aspects such as medical records confidentiality, it is an important consideration of modern times to secure medical information like CT-scan, X-Ray, and MRI when transmitted across public networks. Combining watermarking and BioHashing in medical image protection provides the means to safely transmit data over the internet and enhance authentication. In this context, we present a BioHashing and watermarking-based technique capable of providing integrity, authenticity, and confidentiality to different medical images. At the sender side, first, the BioHashing method is used to generate the watermark which is then embedded in weber local descriptor excitation component images using discrete cosine transform. The proposed technique meets all the requirements of robustness and imperceptibility. The experimental and comparison results demonstrate that the proposed method is superior to existing methods.

A Robust Chaos-Based Technique for Medical Image Encryption

Transmission and storage of medical data using cloud-based Internet-of-health-systems (IoHS) necessitate important prerequisites, such as secrecy, legitimacy, and integrity. This paper recommends a novel hybrid encryption/decryption scheme that can be applied in e-healthcare, or IoHS, for the protection of medical images. The proposed system explores innovative perturbation algorithms that utilize novel chaotic maps. The proposed perturbation-based data encryption is employed in both rounds of confusion and diffusion to cope the drawbacks of traditional chaos-based confusion and diffusion architectures. Particularly, (i) the new maps parameters and chaotic sequences are used to control the permutation and diffusion properties of the scheme, and (2) the derived properties control pixel shuffling and operations of substitution. Different techniques and tests are used to analyzed the chaotic behaviors of the proposed system, including the bifurcation diagram, Lyapunov exponents, as well as the NIST and DIEHARD tests. Moreover, evaluation using various test images indicated that the proposed cryptosystem is fast, have high efficiency, showed high robustness and protection of medical images, and documented the good ability to withstand a variety of cyber-attacks. Furthermore, quantitative results using benchmark color and greyscale images prove the high security levels, sensitivity, and low residual intelligibility with high quality recovered data of our technique than several typical and state-of-the-art encryption schemes. This has been documented using statistical and security analysis metrics, such as number of pixels change rate (NPCR, 99.814%), unified average changing intensity (UACI, 33.694%), peak signal-to-noise-ratio (PSNR, 7.723), and Shannon entropy (7.998).

Medical Image Transmission Using Novel Crypto-Compression Scheme

The transmission of medical records over indiscrete and open networks has caused an increase in fraud involving stealing patients’ information, owing to a lack of security over these links. An individual’s medical documents represent confidential information that demands strict protocols and security, chiefly to protect the individual’s identity. Medical image protection is a technology intended to transmit digital data and medical images securely over public networks. This paper presents some background on the different methods used to provide authentication and protection in medical information security. This work develops a secure cryptography-based medical image reclamation algorithm based on a combination of techniques: discrete cosine transform, steganography, and watermarking. The novel algorithm takes patients’ information in the form of images and uses a discrete cosine transform method with artificial intelligence and watermarking to calculate peak signal-to-noise ratio values for the images. The proposed framework uses the underlying algorithms to perform encryption and decryption of images while retaining a high peak signal-to-noise ratio value. This value is hidden using a scrambling algorithm; therefore, a unique patient password is required to access the real image. The proposed technique is demonstrated to be robust and thus able to prevent stealing of data. The results of simulation experiments are presented, and the accuracy of the new method is demonstrated by comparisons with various previously validated algorithms.

Secure Watermarking Scheme for Color DICOM Images in Telemedicine Applications

Teleradiology plays a vital role in the medical field, which permits transmitting medical and imaging data over a communication network. It ensures data reliability and provides convenient communication for clinical interpretation and diagnostic purposes. The transmission of this medical data over a network raises the problems of legal, ethical issues, privacy, and copyright authenticity. The copyright protection of medical images is a significant issue in the medical field. Watermarking schemes are used to address these issues. A gray-level or binary image is used as a watermark frequently in color image watermarking schemes. In this paper, the authors propose a novel non-blind medical image watermarking scheme based on 2-D Lifting Wavelet Transform (LWT), Multiresolution Singular Value Decomposition (MSVD), and LU factorization to improve the robustness and authenticity of medical images. In this scheme, multiple color watermarks are embedded into the colored DICOM (Digital Imaging and Communications in Medicine) images obtained from Color Doppler images (DICOM format), and the average results achieved by our proposed scheme is 46.84 db for Peak Signal-to-Noise Ratio (PSNR), 37.46 db for Signal-to-Noise Ratio (SNR), 0.99 for Quality of Image and 0.998 for Normalized Correlation for various image processing attacks. These results make our watermarking technique an ideal candidate for medical image watermarking.

A faster outsourced medical image retrieval scheme with privacy preservation

With the rapid development of computer technology and medical imaging technology, medical images present an explosive growth. To save storage and computation overhead, hospitals often choose to outsource digital medical images to cloud server. Since medical images are a major auxiliary means for doctors’ diagnosis or medical researchers’ study, the secure retrieval of outsourced medical images is especially important. To address this problem, we propose a Faster outsourced Medical Image Retrieval scheme with privacy preservation (FMIR) in this paper. FMIR first makes a simple classification to outsourced medical images, which narrows the retrieval range and improves the retrieval efficiency compared with the existing unclassified retrieval schemes. Second, FMIR implements a lightweight access control for each class using polynomial-based access control strategy, which provides the fine-grained access control for better privacy protection of medical images. Third, FMIR reduces the interference of random numbers on relevant score to 0, which further improves the accuracy of the retrieval. Finally, the security and performance analysis show that FMIR is secure, accurate and efficient.

Medical Image Authentication by Combining Hash Signature and Watermarking Based on Frequency Domains

Medical images are transmitted to other remote places through the E-healthcare system. The protection of medical images is very crucial. Medical images need to be protected against any modification which the attacker may do through the unsecured channel. It is necessary for inspecting the integrity of the ROI (Region of Interest) of the received medical image prior to the adoption of any diagnostic decision to avoid a misdiagnosis. This paper aimed to confirm the integrity and authenticity of medical images by combining the hash signature with the watermarking technique based on the frequency domains. At first, the medical image is divided into ROI and RONI (Region of Non-Interest). The SLT and DCT transformations are employed together to extract the essential features set from ROI and then combined with EPR (Electronic Patient Registration) to produce a watermark, and then it is encoded by a chaotic map with a secret key to provide a signature. On the other hand, the RONI is mapping into randomly subblocks based on the linear system, and then the Signature is embedded in the chosen block using DWT. The hash code of the recovered image and EPR will be compared with the extracted watermark for integrity and authenticity. The experimental results demonstrate the watermark’s robustness against many of the more aggressive and geometric signal processing distortions without affecting the quality of the original medical image. In particular, compared with state-of-the-art technology, the proposed algorithm improves the average NC value larger than 0.90 under all types of attacks.

A robust zero-watermarking algorithm for lossless copyright protection of medical images

The continuous orthogonal moments of images have become a main tool used in zero-watermarking algorithms in recent years. However, there is a problem that should not be neglected for existing zero-watermarking algorithms, which is known as numerical instability. Because the continuous orthogonal moments used in existing zero-watermarking algorithms are integer-order moments, this problem affects the computational accuracy of such moments and thus affects the performance of zero-watermarking algorithms. Therefore, in this paper, the integer-order radial harmonic Fourier moments (IoRHFMs) are firstly extended to fractional-order radial harmonic Fourier moments (FoRHFMs) to effectively mitigate the problem of numerical instability and improve the computational accuracy of IoRHFMs, and then a FoRHFM-based zero-watermarking algorithm for medical images is proposed to achieve lossless copyright protection of medical images. The experimental results show that this algorithm is highly robust to geometric attacks and common attacks, and is superior to IoRHFM-based and other zero-watermarking algorithms.

Robust zero-watermarking scheme based on novel quaternion radial fractional Charlier moments

In this paper, we propose a zero-watermarking scheme based on a new set of quaternion moments called Quaternion Radial Fractional Charlier Moments (QRFrCMs) for the copyright protection of color medical images. The proposed moments are developed from a new type of discrete orthogonal polynomials called Fractional Charlier Polynomials (FrCPs) and from quaternion theory. The robustness of the proposed scheme is ensured thanks to robustness of the proposed quaternion moments against image processing attacks and geometric attacks where the BCR is greater than 98%. In addition, the scheme uses the fractional order of QRFrCMs and a chaotic system based on two-dimensional CML (2DCML) using mixed linear-nonlinear coupling to provide a high level of security. Experimental results show the superiority of the proposed scheme over other recent schemes in terms of robustness against geometric attacks and common image processing attacks. The proposed scheme can be used for the secure transmission of color medical images over insecure networks.

Medical Image Protection Algorithm Based on Deoxyribonucleic Acid Chain of Dynamic Length

Current image encryption algorithms have various deficiencies in effectively protecting medical images with large storage capacity and high pixel correlation. This article proposed a new image protection algorithm based on the deoxyribonucleic acid chain of dynamic length, which achieved image encryption by DNA dynamic coding, generation of DNA dynamic chain, and dynamic operation of row chain and column chain. First, the original image is encoded dynamically according to the binary bit from a pixel, and the DNA sequence matrix is scrambled. Second, DNA sequence matrices are dynamically segmented into DNA chains of different lengths. After that, row and column deletion operation and transposition operation of DNA dynamic chain are carried out, respectively, which made DNA chain matrix double shuffle. Finally, the encrypted image is got after recombining DNA chains of different lengths. The proposed algorithm was tested on a list of medical images. Results showed that the proposed algorithm showed excellent security performance, and it is immune to noise attack, occlusion attack, and all common cryptographic attacks.

A robust blind medical image watermarking approach for telemedicine applications.

In order to enhance the security of exchanged medical images in telemedicine, we propose in this paper a blind and robust approach for medical image protection. This approach consists in embedding patient information and image acquisition data in the image. This imperceptible integration must generate the least possible distortion. The watermarked image must present the same clinical reading as the original image. The proposed approach is applied in the frequency domain. For this purpose, four transforms were used: discrete wavelets transform, non-subsampled contourlet transform, non-subsampled shearlet transform and discreet cosine transform. All these transforms was combined with Schur decomposition and the watermark bits were integrated in the upper triangular matrix. To obtain a satisfactory compromise between robustness and imperceptibility, the integration was performed in the medium frequencies of the image. Imperceptibility and robustness experimental results shows that the proposed methods maintain a high quality of watermarked images and are remarkably robust against several conventional attacks.

A DWT-SVD based robust digital watermarking for medical image security

In this work, we propose a blind watermarking approach for medical image protection. In this approach, the watermark will be constituted of the Electronic Patient Record and the image acquisition data. In order to enhance the security and guarantee the data integrity, the Electronic Patient Record hash will be added to the watermark. The integration process is based on a DWT-SVD combination, a DWT is applied to the retinal image, then, an SVD is applied to the LL sub-band. The watermark will be then integrated into the least significant bits of the S component obtained by combining the parity of the successive coefficients. Experimental results for imperceptibility and robustness show that the proposed scheme maintains a high quality watermarked image and remains highly robust against several conventional attacks.

A novel zero-watermarking scheme with enhanced distinguishability and robustness for volumetric medical imaging

The authenticity and copyright protection of volumetric medical images has become extremely important when these images are distributed online for diagnosis and education purpose. Compared to the authenticity and copyright protection of conventional images, there are two additional challenges for protecting the volumetric medical images. On one hand, the content of the protected medical images must be distortion-free to ensure unbiased diagnosis. On the other hand, it requires enhanced distinguishability to avoid misclassification of non-protected images into the protected set because volumetric medical images of different persons in the same modality share similar visual structures. To address these issues, a novel multi-slice feature based zero-watermarking scheme with enhanced distinguishability and robustness for volumetric medical imaging is proposed. In this scheme, ring statistics are deployed to guarantee both the watermarking distinguishability and robustness. In addition, an intra-slice variation based mechanism is designed to further enhance the watermarking distinguishability. Finally, a logistic-logistic system based chaotic map is used to ensure the watermarking security. Our experimental results demonstrate that the proposed scheme not only satisfies the lossless quality requirement but also ensures the watermarking distinguishability and robustness, which outperforms the state-of-the-art schemes.

Blind and Robust Watermarking Scheme in Hybrid Domain for Copyright Protection of Medical Images

This work presents a robust watermarking technique in hybrid domain for the copyright claim of medical images. The scheme is a fusion of three popular transforms: Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD). The input image is first separated into region of interest (ROI) and region of non-interest (RONI). The DWT is applied on RONI to get low and high frequency bands. The low frequency band is then segmented into 4×4 blocks. The Human Visual System (HVS) is applied to select the potential blocks for implanting watermark content. Each 4×4 selected block is further subdivided into four 2×2 carrier matrices. The SVD is applied to each carrier matrix. Finally, the hidden information is implanted by altering the largest diagonal singular values of four 2×2 matrices. The technique is blind, so host image is not needed for the extraction of hidden information. The proposed scheme achieves higher values of imperceptibility as well as robustness. Experimental results reveal that the proposed technique outperforms the techniques currently reported in the literature by achieving higher values of imperceptibility in the form of PSNR with value of 44.0567 decibels (dB) and SSIM value of 0.9800. At the same time it achieves excellent values of robustness with maximum NCC value of 1.000 and minimum BER with value of 0.000.

Cellular Neural Network-Based Medical Image Encryption

This article introduces a novel cryptosystem for the protection of medical images which is very essential in teleradiology applications. The proposed cryptosystem is based on Fridrich architecture which uses hyperchaotic cellular neural network (CNN) and DNA technology to perform cryptographic operations. In this paper, cellular neural network crumb coding transform (CNN-CCT) is proposed to perform confusion operation. It is used to shuffle the pixel values randomly. The diffusion operation is achieved by employing cipher block chain (CBC) mode of XOR operation which provides greater efficiency in hardware platforms. The diffusion operation is used to change the pixel values, thereby achieving the higher security. Simulation and comparison results infer that the proposed cryptosystem is robust against various cryptographic attacks and competitive with the state-of-the-art encryption schemes.

A Novel Selective Encryption Method Based on Skin Lesion Detection

Due to the semitrusted cloud, privacy protection of medical images in medical imaging clouds has become a precondition. For the privacy of patients and the security of medical images in the cloud, this paper proposes a selective encryption based on DNA sequence and chaotic maps for skin lesion image. Initially, we design a transition region-based level set evolution functional which is merged into a variational level set expression with two extra energy functionals, to segment skin lesion image. Once skin lesion detection has been performed, the detected skin lesion pixels are encrypted by employing chaotic systems and DNA sequences. We apply 2D-LASM and 1D-LSS to produce the pseudorandom sequences and use the hash function of the plaintext image to calculate the secret keys of the encryption system. Results demonstrate that the proposed segmentation method is particularly suitable for the detection of skin lesion images with strong noise and complex background. Meanwhile, security analysis also reveals that this selective encryption has a large security key space and high sensitivity to the plaintext image and the secret key.

A DWT based watermarking approach for medical image protection

In order to contribute to the security of medical image, we present in this paper a blind and robust watermarking technique that allows the integration of the electronic patient’s record into a computerized tomography scan. In this approach, a discrete wavelet transform is applied to the image before the integration process, then, a topological reorganization of the coefficients of the LL sub-bands is done by the ZigZag scanning method. The obtained coefficients are then combined to integrate the watermark bits. A hash of the electronic patient record being integrated in the image, the integrity of the watermark can easily be verified. After the evaluation of our approach in terms of invisibility and robustness, the experimental results obtained show that our approach offers excellent imperceptibility (with a PSNR above 70 dB) and very good robustness against several geometric and destructive attacks.

Research on the Practical Classification and Privacy Protection of CT Images of Parotid Tumors based on ResNet50 Model

Parotid gland disease is one of the main causes of facial paralysis, and parotid gland tumor is a great threat to the life of patients. The main diagnostic way of parotid diseases is imaging examination, so it is of great significance for the rapid classification of parotid image. In conclusion, 51 CT images of parotid malignant tumors and 101 CT images of parotid pleomorphic adenomas are selected as the research data set, and an intelligent and efficient machine learning algorithm is proposed for the practical classification of parotid images. At the same time, this paper also explores the privacy protection of medical images. Based on the advantages of deep learning, such as no feature engineering, strong adaptability and easy conversion, ResNet50 model in deep learning is selected as the basic network framework to achieve the purpose of rapid classification of parotid CT images. This is the first time that ResNet50 classification algorithm is applied to the practical classification of parotid tumor CT images. The results show that the accuracy of the test set converges to 90% when the model is iterated 1000 times, which also proves that this study has certain practical significance and application value for the auxiliary diagnosis of parotid gland tumor and other head and neck tumors. Simultaneously, this paper also explores the application of desensitization strategy in CT images of parotid tumors, which improves the performance of the model and also greatly protects the privacy of patients, and has a good application prospect in medical big data.

Study and Analysis of Digital Watermarking for Medical Images

In the recent years, Internet users are increased tremendously. Hence, it demands a secure communication for different Internet services. Data sharing such as audio, video, image and documents are an integral part, which needs to be secure during transmission. In healthcare applications, the patient's information requires security and integrity during transfer of data from one place to another. The watermarking techniques can improve data protection and content enrichment of medical images. In this article, the literature study is carried out to show the frequency domain techniques are more robust than spatial domain. Apart from this, a brief and comparative analysis of watermarking technique with different image size is presented to show the trade-off between capacity and perceptibility of the image.