Medical Image Security Research Articles

A robust medical image cryptosystem based on hyperbolic chaotic system and circular bit-shifting-flipping techniques

Due to the hasty growth of digital technology, securing medical images for transmission and storage is a significant issue. Information on the texture of medical images is essential for diagnosis. Hence, protecting these content has become a great challenge in the e-Healthcare system. So, we propose a novel dynamic circular bit shifting and bit-flipping process in the diffusion phase of the image cryptosystem to provide high level protection for the medical image. In the developed framework, at first, the hyperbolic chaotic map is executed for generating three hyper chaotic series and these chaotic series are utilized for encryption process. In encryption process, primarily the secret image is confused by utilizing any two chaotic series, then the confused image is divided into blocks and each block is diffused by executing the proposed dynamic circular bit shifting and bit-flipping process which greatly increases the probability and complexity level of the developed cryptosystem. Evaluation using different medical images represents that the proposed model has robustness, high efficiency, key sensitivity and also has the ability to withstand different attacks. This has been represented using the results of security measures and statistical analysis, such as Shannon entropy (7.997), CC (< 0), NPCR (99.63 %), UACI (37.14 %), SSIM (< 0), FSIM (< 0.5), PSNR (6.88). These results prove that the developed medical crypt can efficiently provide better security for medical images utilized in real time medical application like e-Healthcare and telemedicine.

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.

High-capacity data hiding for medical images based on the mask-RCNN model

This study introduces a novel approach for integrating sensitive patient information within medical images with minimal impact on their diagnostic quality. Utilizing the mask region-based convolutional neural network for identifying regions of minimal medical significance, the method embeds information using discrete cosine transform-based steganography. The focus is on embedding within “insignificant areas”, determined by deep learning models, to ensure image quality and confidentiality are maintained. The methodology comprises three main steps: neural network training for area identification, an embedding process for data concealment, and an extraction process for retrieving embedded information. Experimental evaluations on the CHAOS dataset demonstrate the method’s effectiveness, with the model achieving an average intersection over union score of 0.9146, indicating accurate segmentation. Imperceptibility metrics, including peak signal-to-noise ratio, were employed to assess the quality of stego images, with results showing high capacity embedding with minimal distortion. Furthermore, the embedding capacity and payload analysis reveal the method’s high capacity for data concealment. The proposed method outperforms existing techniques by offering superior image quality, as evidenced by higher peak signal-to-noise ratio values, and efficient concealment capacity, making it a promising solution for secure medical image handling.

Data Hiding and Authentication Scheme for Medical Images Using Double POB

To protect the security of medical images and to improve the embedding ability of data in encrypted medical images, this paper proposes a permutation ordered binary (POB) number system-based hiding and authentication scheme for medical images, which includes three parts: image preprocessing, double hiding, and information extraction and lossless recovery. In the image preprocessing and double hiding phase, firstly, the region of significance (ROS) of the original medical image is segmented into a region of interest (ROI) and a region of non-interest (RONI). Then, the bit plane of the ROI and RONI are separated and cross-reorganization to obtain two new Share images. After the two new Share images are compressed, the images are encrypted to generate two encrypted shares. Finally, the embedding of secret data and attaching of authentication bits in each of these two encrypted shares was performed using the POB algorithm. In the information extraction and lossless recovery phase, the POBN algorithm is first used to extract the authentication bits to realize image tamper detection; then, the embedded secret message is extracted, and the original medical image is recovered. The method proposed in this research performs better in data embedding and lossless recovery, as demonstrated by experiments.

Secure Medical Image Authentication Using Zero-Watermarking based on Deep Learning Context Encoder

Secure Medical Image Authentication Using Zero-Watermarking based on Deep Learning Context Encoder

Enhancing Medical Image Security with FPGA-Accelerated LED Cryptography and LSB Watermarking

Enhancing Medical Image Security with FPGA-Accelerated LED Cryptography and LSB Watermarking

Genetic Crossover at the RNA Level for Secure Medical Image Encryption

Genetic Crossover at the RNA Level for Secure Medical Image Encryption

New optimized chaotic encryption with BCOVIDOA for efficient security of medical images in IoMT systems

New optimized chaotic encryption with BCOVIDOA for efficient security of medical images in IoMT systems

Foveal Avascular Zone Image Encryption using Pixel Scrambling Combination Technique for Medical Image Security

Data theft from year to year has increased in the era of big data and society 5.0. One area that requires data security is patient medical data. Medical image data security must be done to protect medical data security from data theft by third parties so that they cannot access the data. The development of Diabetic Retinopathy (DR) is also increasing every year. Determining the severity of DR is done by detecting the Foveal Avascular Zone (FAZ). Encryption is the process of changing a plain image into a cipher image. In this study, we compared the results of image quality and encryption time between the Vigenere Cipher method and a combination of pixel scrambling. The average encryption time of the tested FAZ images is 3.20 seconds. This result proves that the pixel combination method has a faster encryption time than the Vigenere Cipher. Vigenere Cipher encryption time is 4.96 seconds. The existence of the FAZ area with the pixel combination randomization method of the encryption process is also invisible, so third parties will not know about its existence.

RISE: Rubik’s cube and image segmentation based secure medical images encryption

RISE: Rubik’s cube and image segmentation based secure medical images encryption

CloudSec: A Lightweight and Agile Approach to Secure Medical Image Transmission in the Cloud Computing Environment

CloudSec: A Lightweight and Agile Approach to Secure Medical Image Transmission in the Cloud Computing Environment

Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer

With the rapid development of medical imaging technology and computer technology, the medical imaging artificial intelligence of computer-aided diagnosis based on machine learning has become an important part of modern medical diagnosis. With the application of medical image security technology, people realize that the difficulty of its development is the inherent defect of advanced image processing technology. This paper introduces the background of colorectal cancer diagnosis and monitoring, and then carries out academic research on the medical imaging artificial intelligence of colorectal cancer diagnosis and monitoring and machine learning, and finally summarizes it with the advanced computational intelligence system for the application of safe medical imaging.In the experimental part, this paper wants to carry out the staging preparation stage. It was concluded that the staging preparation stage of group Y was higher than that of group X and the difference was statistically significant. Then the overall accuracy rate of multimodal medical image fusion was 69.5% through pathological staging comparison. Finally, the diagnostic rate, the number of patients with effective treatment and satisfaction were analyzed. Finally, the average diagnostic rate of the new diagnosis method was 8.75% higher than that of the traditional diagnosis method. With the development of computer science and technology, the application field was expanding constantly. Computer aided diagnosis technology combining computer and medical images has become a research hotspot.

Enhancing robustness and security of medical images through composite watermarking method

Enhancing robustness and security of medical images through composite watermarking method

Design and Implementation of Fuzzy Logic-Based Key Exchange Protocol in Medical Image Cryptographic Protection Scheme

Images may be protected from hackers and attackers with the use of steganography. The rapid expansion of the internet has led to the widespread distribution of vast quantities of multimedia content, including photos, movies, and audio files, via various online platforms. To ensure the safety of sensitive information while it is in transit and upon receipt, a high degree of security is required. During the patient scanning procedure, hospitals and scan centers save many pictures of patients on personal computers. Protection from strangers who may see the patients' scanned photos would be necessary for this. Therefore, scan centers and hospitals all over the globe rely heavily on medical image security. The proposed technique includes Fuzzy Logic-Based Key Exchange Protocol in Medical Image Cryptographic Protection Scheme. To provide the utmost protection for the medical pictures, the cover image incorporates the secret image. At the outset, we standardize the cover and hidden photos. The cover image for this thesis might be a picture of nature or a benchmark; the hidden image, on the other hand, is a medical image in grayscale or binary format. After that, the normalized picture is processed using DWT. The hidden picture is embedded into the cover image using a fuzzy-based edge-related steganography approach, which uses these altered coefficients. To get the stego image, the embedded picture is normalized in the reverse direction. Additionally, this study suggests DT-CWT transform based picture security. Part one of the suggested approach to picture security is image steganography, and part two is picture cryptography. Module 1 uses the DT-CWT transform to fuse the coefficients of the cover picture with the hidden image. After that, the steganography picture is subjected to module 2, which is based on the IE calculation. Analysis of experimental data for the suggested picture security approach revealed improved outcomes for encrypted communication.

A SURF and SVD-based robust zero-watermarking for medical image integrity.

Medical image security is paramount in the digital era but remains a significant challenge. This paper introduces an innovative zero-watermarking methodology tailored for medical imaging, ensuring robust protection without compromising image quality. We utilize Sped-up Robust features for high-precision feature extraction and singular value decomposition (SVD) to embed watermarks into the frequency domain, preserving the original image's integrity. Our methodology uniquely encodes watermarks in a non-intrusive manner, leveraging the robustness of the extracted features and the resilience of the SVD approach. The embedded watermark is imperceptible, maintaining the diagnostic value of medical images. Extensive experiments under various attacks, including Gaussian noise, JPEG compression, and geometric distortions, demonstrate the methodology's superior performance. The results reveal exceptional robustness, with high Normalized Correlation (NC) and Peak Signal-to-noise ratio (PSNR) values, outperforming existing techniques. Specifically, under Gaussian noise and rotation attacks, the watermark retrieved from the encrypted domain maintained an NC value close to 1.00, signifying near-perfect resilience. Even under severe attacks such as 30% cropping, the methodology exhibited a significantly higher NC compared to current state-of-the-art methods.

Image encryption with leveraging blockchain-based optimal deep learning for Secure Disease Detection and Classification in a smart healthcare environment

&lt;abstract&gt; &lt;p&gt;Blockchain (BC) in healthcare can be used for sharing medical records and secure storage and other confidential data. Deep learning (DL) assists in disease recognition through image analysis, specifically in detecting medical conditions from images. Image encryption ensures the security and privacy of medical images by encrypting the image before sharing or storage. The combination of image encryption, BC, and DL provides an efficient and secure system for medical image analysis and disease detection in healthcare. Therefore, we designed a new BC with an Image Encryption-based Optimal DL for Secure Disease Detection and Classification (BIEODL-SDDC) technique. The presented BIEODL-SDDC technique enables the secure sharing of medical images via encryption and BC technology with a DL-based disease classification process. Furthermore, the medical image encryption process took place using the ElGamal Encryption technique with a giraffe kicking optimization (GKO) algorithm-based key generation process. In addition, BC-based smart contracts (SCs) were used for the secure sharing of medical images. For the disease detection process, the BIEODL-SDDC technique encompassed EfficientNet-B7-CBAM-based feature extraction, Adam optimizer, and a fully connected neural network (FCNN). The experimental validation of the BIEODL-SDDC technique was tested on medical image datasets and the outcome highlighted an enhanced accuracy outcome of 94.81% over other techniques.&lt;/p&gt; &lt;/abstract&gt;

Application of Zero-Watermarking Scheme Based on Swin Transformer for Securing the Metaverse Healthcare Data.

The existing medical image privacy solutions cannot completely solve the security problems created by applying the metaverse healthcare system. A robust zero-watermarking scheme based on the Swin Transformer is proposed in this paper to improve the security of medical images in the metaverse healthcare system. This scheme uses a pretrained Swin Transformer to extract deep features from the original medical images with a good generalization performance and multiscale, and binary feature vectors are generated by using the mean hashing algorithm. Then, the logistic chaotic encryption algorithm boosts the security of the watermarking image by encrypting it. Finally, an encrypted watermarking image is XORed with the binary feature vector to create a zero-watermarking, and the validity of the proposed scheme is verified through experimentation. According to the results of the experiments, the proposed scheme has excellent robustness to common attacks and geometric attacks, and implements privacy protections for medical image security transmissions in the metaverse. The research results provide a reference for the data security and privacy protection of the metaverse healthcare system.

DICOM Medical Image Security with DNA- Non-Uniform Cellular Automata and JSMP Map Based Encryption Technique

DICOM Medical Image Security with DNA- Non-Uniform Cellular Automata and JSMP Map Based Encryption Technique

Novel substitution-box generation using group theory for secure medical image encryption in E-healthcare

&lt;abstract&gt; &lt;p&gt;With the increasing need for secure transmission and storage of medical images, the development of robust encryption algorithms is of paramount importance. Securing sensitive digital medical imagery information during transmission has emerged as a critical priority in the e-Healthcare systems. Recent research has highlighted the significance of developing advanced medical image encryption algorithms to ensure secure transmission during telediagnosis and teleconsultations. In this study, we propose a novel medical image encryption algorithm which is based on a novel substitution-box generation algebraic method using a combination of a multiplicative cyclic group with an order of 256 and a permutation group with a large order. To evaluate the security performance of the proposed generated S-box, various standard security indicators are assessed and analyzed. The newly proposed medical image encryption algorithm utilizes the generated S-box, along with bit-plane slicing, circular shifting, and XOR operations, to achieve enhanced security and robustness for encrypting sensitive imagery data. In order to assess the effectiveness of the proposed encryption algorithm, a comprehensive benchmarking analyses, specifically designed for evaluating image encryption schemes, have been conducted. The results obtained from the comparison and other analyses serve to validate the optimal features and high cryptographic strength exhibited by the proposed method. Hence, the proposed algorithm demonstrates significant effectiveness and holds considerable promise in the realm of medical image encryption for secure e-Healthcare systems.&lt;/p&gt; &lt;/abstract&gt;

A Novel Memristive Multiscroll Multistable Neural Network with Application to Secure Medical Image Communication

A Novel Memristive Multiscroll Multistable Neural Network with Application to Secure Medical Image Communication