Medical Image Protection Research Articles

Robust zero-watermarking algorithm for multi-medical images based on FFST-Schur and Tent mapping

Aiming at issues such as the difficulty of centralized protection of multiple medical images and the high storage cost caused by repetitive operations, we proposed a robust zero-watermarking algorithm for multiple medical images based on fast finite Shearlet transform (FFST)-Schur and Tent mapping. First, multiple medical images were normalized, and the processed images were fused using a gray-weighted average fusion method for image fusion. Second, an FFST is applied to the fused image to divide the low-frequency sub-bands into blocks of the same size and perform Schur decomposition, which generates a feature image using the magnitude between the largest eigenvalue of each block and the overall average value. Finally, an image encryption algorithm based on Tent mapping is proposed to encrypt the logo image, and the encrypted image and feature image are then subjected to an exclusive OR operation to produce a zero-watermarking image. Experimental results demonstrate that the proposed approach effectively safeguards against standard image processing and geometric and combinatorial attacks. The average performance of the anti-attack algorithm was improved by approximately 3.2 % compared to similar algorithms, indicating the superior robustness of the proposed algorithm.

An improved robust algorithm for optimisation-based colour medical image watermarking

Recently, significant attention has been focused on copyright protection of medical images, causing watermarking to become a prevalent research topic. In this paper, we propose an optimisation-based robust watermarking algorithm for the copyright protection of colour medical images. Initially, we decompose a host medical image by lifting wavelet transform (LWT), followed by discrete cosine transform (DCT) into transformed coefficients, and then embed multiple watermarks into the transformed host image by using hybrid optimisation algorithms. To further improve security and reduce the channel noise distortion, we then apply hashing and Hamming code to image and text mark, respectively, before the embedding process. Finally, we utilise 2D chaotic map to encrypt the marked media, aiming to achieve enhanced security and confidentiality. The experimental outcome shows satisfactory invisibility, watermark payload and robustness against image-processing attacks. Furthermore, through numerous comparative experiments with nine state-of-the-art algorithms on two standard datasets, the effectiveness of the proposed algorithm is demonstrated.

Protection of medical images through MFDCuT and SA-BSO

Protection of medical images through MFDCuT and SA-BSO

Enhanced Security in Medical Imaging: A Novel Watermarking and Compression Approach

Medical images, including magnetic resonance imaging (MRI), ultrasound (US), computerized tomography (CT), X-rays, and electrocardiography (ECG), each have distinct benefits and drawbacks. Accurate identification of these images is crucial for maintaining patient-specific data integrity. This study proposes a novel watermarking technique that employs Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) to enhance the security, confidentiality, and integrity of medical images. Previous research by Badshah et al. underlines that digital watermarking significantly bolsters the protection of medical images. Additionally, we incorporate Run Length Encoding (RLE) as a compression method to efficiently reduce data memory requirements. The implementation of these techniques demonstrated a marked improvement in the Peak Signal-to-Noise Ratio (PSNR), increasing by up to 5 dB in watermarked images compared to non-watermarked ones, indicating enhanced imperceptibility. Moreover, the file size reduction achieved through our compression approach ranged from 15% to 30%, ensuring that high-quality images consume less storage space. These advancements facilitate the secure and efficient handling of medical image data, supporting their use in clinical environments.

Multimodal Fusion With Optimized Embedding Strength for Consumptive Medical Image Protection

Multimodal Fusion With Optimized Embedding Strength for Consumptive Medical Image Protection

Privacy-preserving cryptographic solutions for medical image protection : The secure force algorithm and intelligent encryption systems

Privacy-preserving must comply with regulatory requirements, safeguard patient privacy, secure data integrity, enable evolving technologies, and guarantee moral and reliable healthcare practices for healthcare applications. By adopting these measures, the healthcare industry may effectively harness the advantages offered by contemporary technology while simultaneously ensuring the protection of patient confidentiality. In this paper, a Secure Force (SF) Encryption Algorithm is modified and improved to guarantee data confidentiality, integrity, and privacy for healthcare applications. Furthermore, SF is also integrated into Intelligent Encryption Systems to augment the protection and confidentiality of confidential patient information. A thorough examination of the algorithm’s efficacy is performed using MATLAB regarding computational efficiency, security, and influence on data entropy. Results demonstrate the algorithm’s ability to preserve patient anonymity, protect sensitive data, and augment data security. Addressing these vital aspects, the research contributes to the ongoing effort to secure medical data in a rapidly evolving healthcare landscape.

Medical image protection using a data-hiding technique based on integer wavelets

Medical image protection using a data-hiding technique based on integer wavelets

Multimodal medical image protection algorithm based on 3D-CICCM and Fresnel transform

Multimodal medical image protection algorithm based on 3D-CICCM and Fresnel transform

Adaptive multiple zero-watermarking based on local fast quaternion radial harmonic Fourier moments for color medical images

The copyright protection of medical images is receiving increasing attention. Zero-watermarking schemes are a critical technology for copyright protection of medical images and show better imperceptibility performance than embedded watermarking schemes. However, existing zero-watermarking schemes still have some limitations that prevent them from fully exploiting their potential, including failure against complex desynchronization attacks and high computational complexity. To this end, we propose a novel zero-watermarking scheme based on fast quaternion radial harmonic Fourier moments (FQRHFMs) that is resistant to desynchronization attacks. Specifically, this article formulates multiple local feature regions (LFRs) based on a speeded-up robust feature (SURF) operator and provides a fast computation method for QRHFMs by using fast Fourier transform (FFT). The magnitudes of FQRHFMs are computed in each incomplete overlapping LFR for generating multiple zero-watermarks to resist desynchronization attacks. Experimental results show that the proposed scheme achieves state-of-the-art robustness against various desynchronization attacks (e.g., translation, cropping, and length-width ratio changing), and outperforms existing local zero-watermarking frameworks with regard to accuracy and efficiency.

Watermarking of Frequency and Steganography for Protection of Medical Images Based on Bacterial Foraging Optimization and Genetic Algorithm

Watermarking of Frequency and Steganography for Protection of Medical Images Based on Bacterial Foraging Optimization and Genetic Algorithm

Zero-watermark scheme for medical image protection based on style feature and ResNet

Zero-watermarking technology is of major interest within the field of medical image copyright protection because this embedding method does not cause loss of images. However, the features used to construct the zero-watermark in existing methods are destroyed after geometric attacks, particularly strong geometric attacks. This destruction causes the final extracted watermark image to lose its details and fail copyright protection. This study proposes a zero-watermark medical image protection scheme based on style features and a residual network to solve this concern. The style features extracted based on deep neural networks are high-order statistics of image details and abstract features and have good robustness against geometric attacks, such as rotation and scale. Simultaneously, this study proposes a zero-watermark verification method based on the residual network to replace the traditional XOR11The full name is exclusive OR, abbreviated as xor, which is a common logical operation in the computer field. operation. The residual network can iteratively learn to extract the watermark image from the zero-watermark image and enhance the quality of the watermark image using the designed loss function. We experimentally demonstrated that the residual network outperformed in reconstructing watermark images. This scheme can resist common geometric and strong image attacks, performance is superior to that of similar zero-watermarking schemes.

A Hybrid Watermarking Approach for DICOM Images Security

A hybrid watermarking approach for DICOM medical image protection is proposed in this paper. The medical image is first separated into two parts: the region used for the diagnosis, called Region Of Interest (ROI), and the remaining, called Region Of Non-Interest (RONI). The patient’s name is then extracted from the header of the DICOM image and pertinent features from the ROI. These data are therefore used to construct a watermark based on a Jacobian model. On the one hand, this watermark is used for the zero watermarking of the ROI. On the other hand, it is divided into blocks that are embedded in the RONI using a linear interpolation technique. Experiment results show a good performance of the proposed approach. The average values of SSIM, PSNR, NC, and BER are respectively 0.98, 71 dB, 0.98, and 0.0054, and the watermark embedding and extraction times do not exceed 12 s.

Copyright protection of medical images: A view of the state-of-the-art research and current developments

Copyright protection of medical images: A view of the state-of-the-art research and current developments

Zero watermarking scheme for privacy protection in e-Health care

E-health care is an emerging field where health services and information are delivered and offered over the Internet. So the health information of the patients communicated over the Internet has to protect the privacy of the patients. The patient information is embedded into the health record and communicated online which also induces degradation to the original information. So, in this article, a zero watermarking scheme for privacy protection is proposed which protects the privacy and also eliminates the degradation done during embedding of patient information into the health record. This method is based on simple linear iterative clustering (SLIC) superpixels and partial pivoting lower triangular upper triangular (PPLU) factorization. The novelty of this article is that the use of SLIC superpixels and PPLU decomposition for the privacy protection of medical images (MI). The original image is subjected to SLIC segmentation and non-overlapping high entropy blocks are selected. On the selected blocks discrete wavelet transform (DWT) is applied and those blocks undergo PPLU factorization to get three matrices, L, U and P, which are lower triangular, upper triangular and permutation matrix respectively. The product matrix is used to construct a zero-watermark. The technique has been experimented on the UCID, BOWS and SIPI databases. The test results demonstrate that this work shows high robustness which is measured using normalized correlation (NC) and bit error rate (BER) against the listed attacks.

Hybrid Security Model for Medical Image Protection in Cloud

A cloud computing environment provides a cost-effective way for end-users to remotely store and retrieve private data through an internet connection anytime and anywhere. The security of these data cannot always be guaranteed because they can only be accessed by the end-user through a third-party interface, making them vulnerable to potential breaches of authentication and data integrity. This paper presents a secure hybrid approach for a medical image stored in the cloud that prioritizes data security and integrity. The suggested model employs a combination of Elliptic Curve Cryptography (ECC) and Advanced Encryption Standard (AES) algorithms to ensure authentication and data integrity. The results demonstrate its superiority over existing methods making the proposed model compliant with regulations of sensitive data stored in the cloud, such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR) that dictate how personal information must be handled, stored, and processed. By contrast with traditional and other hybrid systems, this paper can conclude that this is one of the best methods to guarantee the security of medical images in the cloud.

Advanced 5D logistic and DNA encoding for medical images

ABSTRACT Since patient diagnostic data such as X-rays, MRIs, CT scans, etc. contain very sensitive information about a patient's health, it must be encrypted to prevent unauthorized access. In chaotic-based image encryption methods, the lower-dimensional chaotic maps are insufficient because they can be estimated through some signal estimation technologies; therefore, a higher-order novel 5D logistic chaotic map along with DNA encoding and the Arnold map for medical image encryption is presented to overcome the vital medical breaching that could cost a life. In this work, the advantages of DNA principles and high-dimensional chaotic maps are used to develop an efficient encryption method for the protection of medical images. The strategy is useful due to the fact that the higher order makes the system more complicated and tough to penetrate. The large keyspace up to, anti attack capabilities, and comparison of various standard parameters with different literatures illustrate the algorithm's superiority.

Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

The field of healthcare is considered to be the most promising application of intelligent sensor networks. However, the security and privacy protection of medical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention. Fortunately, digital watermarking provides an effective method to solve this problem. In order to improve the robustness of the medical image watermarking scheme, in this paper, we propose a novel zero-watermarking algorithm with the integer wavelet transform (IWT), Schur decomposition and image block energy. Specifically, we first use IWT to extract low-frequency information and divide them into non-overlapping blocks, then we decompose the sub-blocks by Schur decomposition. After that, the feature matrix is constructed according to the relationship between the image block energy and the whole image energy. At the same time, we encrypt watermarking with the logistic chaotic position scrambling. Finally, the zero-watermarking is obtained by XOR operation with the encrypted watermarking. Three indexes of peak signal-to-noise ratio, normalization coefficient (NC) and the bit error rate (BER) are used to evaluate the robustness of the algorithm. According to the experimental results, most of the NC values are around 0.9 under various attacks, while the BER values are very close to 0. These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods, which indicates it is more suitable for medical image privacy and security protection.

Integer wavelet transform based data hiding scheme for medical image protection

Integer wavelet transform based data hiding scheme for medical image protection

Dynamic key generator based colour medical image protection algorithm using 3D unified chaotic system and dynamic DNA coding

Dynamic key generator based colour medical image protection algorithm using 3D unified chaotic system and dynamic DNA coding

QAPP: A quality-aware and privacy-preserving medical image release scheme

The direct release of medical image may face the dilemma: the privacy protection of medical images inevitably affects the visual quality of images. To balance medical image quality and privacy, this paper proposes a quality-aware and privacy-preserving medical image release scheme, QAPP, which effectively integrates the discrete cosine transform (DCT) with differential privacy (DP). Specifically, QAPP is composed of three phases. First, DCT is applied to each medical image to obtain its cosine coefficients matrix. Second, the original cosine coefficients matrix is compressed into k*k cosine coefficients matrix, which can retain the main features of each image. Third, the appropriate Laplace noise is injected into the formed k*k matrix to achieve differential privacy, and these noise-added coefficients are used to reconstruct the noise-added medical images through inverse DCT. Especially, considering there two error sources affecting the image quality in our work: the compression error caused by DCT, and the injected noise error caused by DP, Therefore, a selection function is proposed to determine the optimal compression dimension k, which can minimize the influence of these two errors to improve the visualization quality of the medical image. Subjective and objective image quality evaluation, and extensive experiments of image classification and segmentation using the real medical image dataset demonstrate that the proposed method QAPP can better balance medical image quality and privacy than other similar DP-based methods.