Medical Image Coding Research Articles

A comparative analysis of chaos theory based medical image steganography to enhance data security

The security of patient data is a critical problem for health networks due to the rising popularity of telehealth services and the requirements for clinical data sharing between surgeons, consultants, and medical groups. In contrast to protecting medical data, which includes the cover (i.e., clinical picture), the requirements for protecting other pertinent data are more general. The paper advocates for data encryption before concealment as a robust strategy to uphold patient privacy during medical information exchange. The primary focus of this analysis revolves around a comprehensive exploration of different steganography methods. Through a meticulous examination of various steganographic techniques, including LSB, PVD, and transform domain approaches, this paper provides a detailed analysis of their strengths and limitations. Objective metrics like PSNR and SSIM are employed to dissect the trade-offs between data security and visual fidelity. The research leads to the conclusion that the diagonal queue-based steganog-raphy methodology, supported by chaotic methods, the Linear Feedback Shift Register (LFSR), and the durable Rabin encryption system, is the best course of action. This method enables autonomous data transmission among numerous contacts while ensuring the highest level of confidentiality and integrity of patient data inside e-health networks. In summary, this study provides a comprehensive solution that protects patient data and speeds data transmission inside the expanding framework of e-health networks in order to meet urgent data security concerns in telemedicine and healthcare data exchange.

Randomly-based Stepwise Multi-Level Distributed Medical Image Steganography

Steganography deals with concealing sensitive information that can either be communicated across a network or stored in a secured location. The concealment of information is accomplished through the carrier, making data access by an unauthorized person more difficult. There are many stenographic techniques that have been used. Unfortunately, the hybrid-multi-level approach was ignored. For this reason, the current research utilized image steganography on a hybrid-multi level involving encryption, data compression, and two-stage high data concealment. The proposed technique can be used to conceal information in medical images without any distortion, allowing flexible and secure transfer capability. After using the Trible DES algorithm to encrypt the secret text at the beginning of the process, the next step involves embedding the secret encrypted cipher message into the host image while keeping the image intact. The findings indicate that the value of PSNR and NCC are satisfactory when compared to the sensitivity of the human eye. As a direct impact, the confidential message is hidden from the adversary. It can be seen that the PSNR value is quite high. Therefore, this indicates that the image after the stenographic process is relatively similar to the original image.

MI-STEG: A Medical Image Steganalysis Framework Based on Ensemble Deep Learning

Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images. On the other hand, the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not. Inspired by previous studies on image steganalysis, this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information. With this purpose in mind, a dataset containing brain Magnetic Resonance (MR) images of healthy individuals and epileptic patients was built. Spatial Version of the Universal Wavelet Relative Distortion (S-UNIWARD), Highly Undetectable Stego (HUGO), and Minimizing the Power of Optimal Detector (MIPOD) techniques used in spatial image steganalysis were adapted to the problem, and various payloads of confidential data were hidden in medical images. The architectures of medical image steganalysis networks were transferred separately from eleven Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet), and Inception-based models. The steganalysis outputs of these networks were determined by assembling models separately for each spatial embedding method with different payload ratios. The study demonstrated the success of pre-trained ResNet, DenseNet, and Inception models in the cover-stego mismatch scenario for each hiding technique with different payloads. Due to the high detection accuracy achieved, the proposed model has the potential to lead to the development of novel medical image steganography algorithms that existing deep learning-based steganalysis methods cannot detect. The experiments and the evaluations clearly proved this attempt.

Refining Medical Image Steganography Scheme Based on Pixels Disparity Value and Huffman Coding

The steganography is a new and challenging method for transmitting confidential information over the internet utilizing a hidden cover object. Developing an efficient picture steganography system is currently fraught with difficulties because of the limited capacity, weak resilience, and imperceptibility of current steganography systems under development. This problem can only be solved by increasing the picture steganography's capability and security levels. Methods: Based on these variables, this work aims to present the state-of-the-art approach known as the Pixels Disparity Value (PDV) that hides patient information reports in the distinct medical photos for the second party. It's recommended that the procedure be broken down into three primary phases, each with its own set of instructions. Results: The statistical and economic evaluations of the suggested approach.

A robust medical image steganography approach based on particle swarm optimization algorithm and quantum walks

Medical information plays an essential task in our everyday lives, in which medical data privacy and security constitute an important issue. The confidentiality of medical data can be achieved by applying one or more encryption and data hiding methods. Amidst the development of quantum computers, most medical data confidentiality techniques may be hacked because their construction is based on mathematical models. Most medical data have a long lifetime exceeding 25 years. Therefore, it is an important issue to design a new medical data hiding technique that has the capability to withstand the probable attacks from the side of quantum or digital devices. In this article, we aim to present a novel medical image steganography strategy based on quantum walks, chaotic systems, and particle swarm optimization algorithm. A 3-D chaotic system and quantum walks are utilized for operating particle swarm optimization algorithm, in which the generated velocity sequence is utilized for substituting the confidential data, and the position sequence is utilized for selecting which position in the hosting image will be employed to host the substituted confidential data. The payload capacity of the suggested mechanism is 2 bits per 1 byte, and the average value for PSNR is 44.1, which is big enough for the naked eye to not differentiate the difference between the carrier image and its stego one.

Secure medical image steganography based on Discrete Wavelet Transformation and ElGamal encryption algorithm

The latest development in computing methodologies and related steganography based applications had created a novel practice such as telemedicine where patient diagnosis images or allied information can be used diagnosis practices that are located in remote areas. In order to provide accurate and effective telemedicine the flawless or seamless biomedical information is required from patient. With respect to this the medical data may remain prone to get corrupted by hackers or might get manipulated when transmitted through an insecure channel. The cryptosystems which are available are not efficient enough to solve the above mentioned issues. Hence, in this research work a much efficient and effective image steganography technique has been proposed in order to hide the confidential and important image information. We have incorporated Discrete Wavelet Transformation (DWT) technique instead of wavelet transform techniques to embed the secret message in the required cover images. Also, to assure continuous communication through insecure channel, a new model called ElGamal encryption cryptosystem model has been implemented which contains the steganography scheme which has been proposed and developed. To maintain the content of Region of Interest (ROI), Region of Non- Interest (RONI) is used to embed the secret information. The overall performance analysis reveals that the DWT with ElGamal encryption provides more efficient and also have high embedding capacity, imperceptibility when compared to other methods like Hamming code, Syndrome-Trellis Code (STC) and Rivest-Shamir- Adleman (RSA) based methods.

Security of medical images over insecure communication channels using zero-steganography

Secure and private communication has gained much focus on the international arena recently due to the ability of certain organizations to eavesdrop on private communication. This ability is further increased in the Internet of Medical Things scenario where medical images and private data are shared over insecure channels. This work is focused on a new and exciting question related to medical image steganography: is it possible to send hidden information through a cover medium, that is a medical image, without making any alteration to the cover medium in an effective manner? The proposed work has shown that it is possible to send hidden information through a medical image without making any alteration to the original medical image. This solves two problems at the same time. The first problem is that privacy and security approaches that depend on watermarking and steganography usually need some sort of modification to the medical images. The second is the knowledge that there is steganography taking place is completely hidden as no change is made to the medical image. Experiments have shown that the proposed approach under investigation demonstrates effective robustness against attacks like filtering, compression, and addition of noise.

Low-complexity three-dimensional discrete Hartley transform approximations for medical image compression

The discrete Hartley transform (DHT) is a useful tool for medical image coding. The three-dimensional DHT (3D DHT) can be employed to compress medical image data, such as magnetic resonance and X-ray angiography. However, the computation of the 3D DHT involves several multiplications by irrational quantities, which require floating-point arithmetic and inherent truncation errors. In recent years, a significant progress in wireless and implantable biomedical devices has been achieved. Such devices present critical power and hardware limitations. The multiplication operation demands higher hardware, power, and time consumption than other arithmetic operations, such as addition and bit-shifts. In this work, we present a set of multiplierless DHT approximations, which can be implemented with fixed-point arithmetic. We derive 3D DHT approximations by employing tensor formalism. Such proposed methods present prominent computational savings compared to the usual 3D DHT approach, being appropriate for devices with limited resources. The proposed transforms are applied in a lossy 3D DHT-based medical image compression algorithm, presenting practically the same level of visual quality (>98% in terms of SSIM) at a considerable reduction in computational effort (100% multiplicative complexity reduction). Furthermore, we implemented the proposed 3D transforms in an ARM Cortex-M0+ processor employing the low-cost Raspberry Pi Pico board. The execution time was reduced by ∼70% compared to the usual 3D DHT and ∼90% compared to 3D DCT.

A Comprehensive Review on Medical Image Steganography Based on LSB Technique and Potential Challenges

The rapid development of telemedicine services and the requirements for exchanging medical information between physicians, consultants, and health institutions have made the protection of patients’ information an important priority for any future e-health system. The protection of medical information, including the cover (i.e. medical image), has a specificity that slightly differs from the requirements for protecting other information. It is necessary to preserve the cover greatly due to its importance on the reception side as medical staff use this information to provide a diagnosis to save a patient's life. If the cover is tampered with, this leads to failure in achieving the goal of telemedicine. Therefore, this work provides an investigation of information security techniques in medical imaging, focusing on security goals. Encrypting a message before hiding them gives an extra layer of security, and thus, will provide an excellent solution to protect the sensitive information of patients during the sharing of medical information. Medical image steganography is a special case of image steganography, while Digital Imaging and Communications in Medicine (DICOM) is the backbone of all medical imaging divisions, whereby it is most broadly used to store and transmit medical images. The main objective of this study is to provide a general idea of what Least Significant Bit-based (LSB) steganography techniques have achieved in medical images.

Medical JPEG image steganography method according to the distortion reduction criterion based on an imperialist competitive algorithm

In most of the digital steganography methods provided for natural digital images, the embedding of the confidential message is based on the minimisation of the defined distortion functions. It is often done based on choosing the most optimal criterion of distortion. Although the distortion functions are designed innovatively, steganography algorithms will be optimal. In such approaches, embedding interactions are often overlooked. Unlike usual images that have areas with a variety of tissue features, there are many smooth areas in medical images that will make the changes more visible if they are manipulated. Therefore, this study presents an adaptive approach that comes from the interactions between the changes made during the embedding algorithm to reduce the probability of recognising the message embedded in medical images and reducing the distortion caused by embedding in a discrete cosine transform space and based on the imperialist competitive algorithm for joint photographic experts group images, especially in medical images due to the importance of information steganography in them. The results obtained show the high efficiency of the proposed algorithm in comparison with the state-of-the-art methods that are presented in this area.

Single- and Multi-Step Image Enlargement for Medical Image Coding

Background: Some interpolators cannot be used in an image magnification problem in a freely scalable form. For instance, when we want to magnify an image to a 16-time bigger scale, some interpolators have to do this process in two steps including two 4-time magnification steps, however, some are able to do it directly. Materials and Methods: For generating data of this study, MATLAB as a simulator has been used. Bi-; Linear (BL) and Cubic Convolution (CC) interpolators are the two applied re-samplers in the reconstruction of digital images. Results: Data shows that the performance of both free-size interpolators (BL and CC) is obviously different in both direct and indirect pixel reconstruction. Conclusion: The acquired data shows a less error in the condition of direct interpolation. The relative results of experiments are different from the type of core interpolators (BL and CC).

Segmentation based image compression of brain magnetic resonance images using visual saliency

The coding of medical images is of paramount importance due to an exponential increase in digital imaging data. The realization of these coding methods is highly challenging as the key fact is to maintain the visual quality by preserving the clinically critical information and also reducing the storage space at the same time. To address these issues, the current paper proposes an efficient non-uniform compression algorithm based on visual saliency that emulates the human visual system. This hybrid technique works in two phases. In the first phase, an automatic saliency-based Fuzzy C-Means clustering algorithm (sal_FCM) is designed for Region of Interest (ROI) detection and extraction. In the second phase, the encoding of ROI and the background are carried out using the SPIHT algorithm (ROI-SPIHT) at a high and low bit rate respectively. To curtail the computational complexity of the proposed ROI-SPIHT algorithm, a wavelet-based on a Lifting scheme is used. The empirical evaluation was carried out on the BRATS dataset where the results suggest that the proposed approach accurately identifies the ROI with comparatively better visual quality and compression ratio.

A Robust Quasi-Quantum Walks-Based Steganography Protocol for Secure Transmission of Images on Cloud-Based E-healthcare Platforms.

Traditionally, tamper-proof steganography involves using efficient protocols to encrypt the stego cover image and/or hidden message prior to embedding it into the carrier object. However, as the inevitable transition to the quantum computing paradigm beckons, its immense computing power will be exploited to violate even the best non-quantum, i.e., classical, stego protocol. On its part, quantum walks can be tailored to utilise their astounding ‘quantumness’ to propagate nonlinear chaotic behaviours as well as its sufficient sensitivity to alterations in primary key parameters both important properties for efficient information security. Our study explores using a classical (i.e., quantum-inspired) rendition of the controlled alternate quantum walks (i.e., CAQWs) model to fabricate a robust image steganography protocol for cloud-based E-healthcare platforms by locating content that overlays the secret (or hidden) bits. The design employed in our technique precludes the need for pre and/or post encryption of the carrier and secret images. Furthermore, our design simplifies the process to extract the confidential (hidden) information since only the stego image and primary states to run the CAQWs are required. We validate our proposed protocol on a dataset of medical images, which exhibited remarkable outcomes in terms of their security, good visual quality, high resistance to data loss attacks, high embedding capacity, etc., making the proposed scheme a veritable strategy for efficient medical image steganography.

Comparison Study and Operation Collapsing Issues for Serial Implementation of Square Matrix Multiplication Approach Suitable in High Performance Computing Environment

Multiplication between two square matrices is one of the fundamental computational approach in the domain of mathematics and computer science where it is fully recognized as a fore most technique for several interdisciplinary domain and sub domains like linear algebra, graph theory, multidimensional graphics, cryptographic computation, convolution neural network, deep learning, digital signal processing, medical image processing, steganography, relativity theory, quantum computing and many others. In a high-performance computing environment computational complexity analysis of matrix multiplication algorithm ensures a powerful paradox that takes a massive data processing approach to the world where problem solution feasibility comes down in respect of operation and time. In our paper we have analyze different methods to multiply two square matrices (like 2×2 matrices) and their arithmetic complexity analysis in a asymptotic flavor along with operation collapsing issues through serial processing technique. We have analytically and experimentally explained and shown using MATLAB 9.3 simulator that fast matrix multiplication approach like Strassen and Winograd perform much better than conventional matrix multiplication algorithm especially for large amount of data.

Secure Text Sharing Using Medical Image Steganography

The increased popularity of digital media has raised serious concerns over its security related issues. Security attacks in the form of eavesdropping, masquerading and tampering and in many other forms is very common nowadays. Data hiding is one of the emerging techniques that aim to provide for security by hiding secret information into the multimedia contents by altering some nonessential components in the host or cover file. Security of data is very important in data communication. Everyday a lot of information is transferred from one user to another on internet and so the possibility of data theft also increases. Steganography provides a solution for the security of information during data transmission. Steganography is the science which makes the valuable information invisible to prevent it from unauthorized user. A steganography system, in general, is expected to meet three key requirements, namely, imperceptibility of embedding, accurate recovery of embedded information, and large payload (payload is the bits that get delivered to the end user at the destination). So in this project an image steganography technique is proposed to hide the documents in image in the transform domain using CMD approach. The document files are carried by the image without revealing the existence to anybody. When the secret information is hidden in the carrier the result is the stego signal. In this work, the results show good quality stego signal and the stego signal is analyzed for different attacks. The stego signals are transferred to multiple receivers based on network strategies. It is found that the technique is robust and it can withstand the attacks. The quality of the stego image is measured by Peak Signal to Noise Ratio (PSNR) and other measurements.

Secure medical image steganography through optimal pixel selection by EH-MB pipelined optimization technique

In today’s world, transmission of information over the channel is not secure for example patient records and other sensitive information. In order to protect this sensitive information, it is coded within the image, audio or text files which is decodable only with the help of a particular key. To enable security to the covert communication and safeguarding the information for securing medical data to avoid medical related cybercrimes, we have proposed a method for medical image steganography using Elephant Herding-Monarch Butterfly (EH-MB) Optimization algorithm for effective selection of pixels for embedding the secret message (i.e. image/text medical report data) in the cover image. Initially, the cover is converted to frequency domain using multilevel DWT, where, the pixel selection is done optimally in the high frequency components using EH-MB algorithm. EH-MB based pixel selection procedure uses a fitness function that depends on the cost function, which calculates the edge, entropy, and intensity of the pixel for evaluating fitness. Simulation was done in the working platform of MATLAB and comparison of the proposed steganography approach was done with the other existing methods in terms of Peak-Signal-to Noise-Ratio and Mean Square Error to prove the effectiveness of the proposed approach.

Effective payload and improved security using HMT Contourlet transform in medical image steganography

This paper implements a novel approach for image steganography based on Hidden Markov Tree (HMT) Contourlet transform. In this paper, the biomedical image considers as a cover image and it is mapped to a specific frequency domain by applying HMT Contourlet transform. Then canny edge detection method implemented to detect the smooth edges to hide the secret data. The secret data is encrypted by using Paillier cryptosystem in a new location of the cover image. Particle Swarm Optimization (PSO) algorithm developed for the selection of the best place to locate the number of particles in a new location. The proposed method prevents the medical image from the various attacks such as rotate, crop, histogram, salt & pepper, blur and resize provides the robustness, thereby reduces to 8.19%, 10.88%, 24.03%, 15.27%, 13.21% and 14.35%. The performance measures of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) are calculated to show the better performance compared with the existing techniques.

Farey Sequence for Error Correcting Codes and Medical Images

A new scheme for Error Correcting Codes and the coding of medical images through Farey Sequence is brought in the paper. It brings out the reduced real point representations and more of integer point representations. The test case and outcomes are explained in the section 4 and 5.

A Multiple Criteria-Based Cost Function Using Wavelet and Edge Transformation for Medical Image Steganography

Abstract With the ever-increasing need for concealing messages within cover media like image, video, and audio, numerous attempts have been developed for steganography. Most of the steganographic techniques perform their embedding operation on the cover image without selecting a better location. The right selection of location for embedding the information can lead to high imperceptibility and robustness. Accordingly, in this paper, we develop a new cost function for estimating the cost of every pixel to identify the good location to embed the message data. The proposed cost estimation procedure utilizes multiple parameters like wavelet coefficient, edge transformation, and pixel intensity. The proposed cost matrix is then utilized to embed the message data into the cover media using an embedding integer. The proposed steganographic technique is experimented with two magnetic resonance brain images, and the results are analyzed with the peak-to-peak signal-to-noise ratio (PSNR) and mean square error. The robustness analysis ensured that the proposed steganographic technique outperforms the existing methods by reaching the maximum PSNR of 72.74 dB.

An evolutionary computing enriched RS attack resilient medical image steganography model for telemedicine applications

The recent advancement in computing technologies and resulting vision based applications have gives rise to a novel practice called telemedicine that requires patient diagnosis images or allied information to recommend or even perform diagnosis practices being located remotely. However, to ensure accurate and optimal telemedicine there is the requirement of seamless or flawless biomedical information about patient. On the contrary, medical data transmitted over insecure channel often remains prone to get manipulated or corrupted by attackers. The existing cryptosystems alone are not sufficient to deal with these issues and hence in this paper a highly robust reversible image steganography model has been developed for secret information hiding. Unlike traditional wavelet transform techniques, we incorporated Discrete Ripplet Transformation (DRT) technique for message embedding in the medical cover images. In addition, to assure seamless communication over insecure channel, a dual cryptosystem model containing proposed steganography scheme and RSA cryptosystem has been developed. One of the key novelties of the proposed research work is the use of adaptive genetic algorithm (AGA) for optimal pixel adjustment process (OPAP) that enriches data hiding capacity as well as imperceptibility features. The performance assessment reveals that the proposed steganography model outperforms other wavelet transformation based approaches in terms of high PSNR, embedding capacity, imperceptibility etc.