Storage Of Medical Images Research Articles

Review of medical image processing using quantum-enabled algorithms

Efficient and reliable storage, analysis, and transmission of medical images are imperative for accurate diagnosis, treatment, and management of various diseases. Since quantum computing can revolutionize big data analytics by providing faster solutions and security tactics, numerous studies in this field have focused on the use of quantum and quantum-inspired algorithms to enhance the performance of traditional medical image processing approaches. This review aims to provide readers with a succinct yet adequate compendium of the advances in medical image processing combined with quantum behaviors for disease diagnosis and medical image security. Some open challenges are outlined, identifying the performance limitations of current quantum technology in their applications, while addressing the short-, medium-, and long-term development plans of this field in designing future quantum healthcare systems. We hope that this review will provide full guidance for upcoming researchers interested in this area and will stimulate further appetite of experts already active in this area aimed at the pursuit of more advanced quantum paradigms in medical image processing applications.

Development of Computed Tomography Quality Control Training Application Model Based on the American College of Radiology Phantom Integrated Website

The use of Viewdex as a radiographic image evaluation tool has limited flexibility for students, especially in the complicated process of installation and input of DICOM ACR image results. These limitations can slow down the learning process and reduce the effectiveness of education on radiological image evaluation. This research aims to develop a web-integrated ACR Phantom-based CT quality control training application, which can overcome the weaknesses of Viewdex and improve efficiency and user experience. The research method used is Research and Development (R&D), which consists of several stages: literature study, product development, field testing, evaluation, and product revision. The application developed simplifies the process of DICOM data import, question coding, and automatic evaluation, improving operational efficiency. Validation of information technology and QA/QC experts shows that the application meets high standards in various aspects with 100% validity. Performance evaluation through pre-test and post-test showed a significant improvement in participants' knowledge and skills, with the Wilcoxon test showing a p-value of 0.005. The developed application has proven to be effective and efficient in improving the usability and learning experience of students, overcoming the weaknesses of Viewdex. Its advanced features enable efficient storage, retrieval, and management of medical images, as well as access and analysis of medical data.

Third- and fifth-order nonlinearities in quantum dot hybrid systems: Influence of graphene and metal nanoparticles

This work examines the third-order and fifth-order nonlinear susceptibilities in a hybrid system including a semiconductor quantum dot, metallic nanoshell, and graphene nanodisk. The research uses the density matrix method to examine the dipole–dipole interaction that occurs due to the applied field. It assumes that there is an increase in the strength of a continuous-wave electromagnetic field inside the quantum dot. The inclusion of the graphene nanodisk greatly amplifies the nonlinear optical reaction of the quantum dot in this combined system, which is governed by the dipole–dipole interaction. The findings indicate a notable disparity in the nonlinear optical reaction of the semiconductor quantum dot when the parameters are modified. The possible applications of this concept include optical sensors, photonic devices, quantum computing, medical imaging, energy harvesting, and high-density data storage. Additionally, it has the potential to progress terahertz technology and simplify the development of new materials with customized optical properties. The substantial rise in nonlinearities inside the hybrid system presents prospects for advancements in diverse technological and scientific domains.

Parametrization of generalized triangle groups and construction of substitution-box for medical image encryption

The construction of strong encryption techniques is crucial to meet the increasing demand for secure transmission as well as storage of medical images. A substitution box (S-Box) is an important component of block ciphers and nonlinearity is an important attribute to consider while designing secure S-boxes. As a result, it is required to create new approaches for producing S-boxes with high non-linearity scores. We present a method of parametrization of the generalized triangle group 〈x,y|x2=y5=wk=1〉 as linear groups, where w=xyxy2xy4 which is extended by the parametrization for triangle group 〈x,y,t|x2=y5=t2=(xt)2=(yt)2=(xy)k=1〉. This parametrization is then used for the construction of a highly nonlinear and secure substitution box designed for 28 elements, tailored specifically for the finite generalized triangle group case with k=2 for θ=64 which is parameter for all homomorphism from H5 to PSL(2,q), possessing an order of 1200. We rigorously evaluate and analyze various common security indicators associated with the proposed substitution box. The proposed S-box is evaluated for picture encryption using various statistical approaches. Comparative analysis and additional scrutiny reveal promising attributes, affirming its suitability, efficacy, and potential applicability in the domain of medical image encryption. Our S-box achieves the necessary conditions for secure communication as well as image encryption, as confirmed by positive examination results.

A lossy compression algorithm for data basis images based on DCT decomposition and diffuse representation

In this manuscript, we present a novel approach for compressing a database of images. Initially, we introduce a new representation that effectively reduces the data required to represent the image, enhancing the efficiency of subsequent compression steps. We apply the Discrete Cosine Transform (DCT) to the new representation and perform thresholding at different levels for a selected image. The DCT is known for concentrating image energy into a few coefficients, making it ideal for compression. By calculating the Peak Signal-to-Noise Ratio (PSNR) variation of the reconstructed image and evaluating the percentage of non-zero coefficients after thresholding, we plot the corresponding curve. These calculations provide insights into the quality of the reconstructed image, ensuring minimal degradation from compression. We repeat the steps with the Discrete Wavelet Transform (DWT), known for its multi-resolution analysis, to compare its performance against the DCT. The curves help us observe which process allows for image reconstruction with only a small percentage of coefficients, achieving significant data reduction. This comparative analysis determines the most effective method for various types of images. After analyzing the digital results from these curves, we generate and discuss the reconstructed image outcomes in detail. The digital and visual results presented at the conclusion demonstrate the robustness of the proposed strategy. We also explore potential applications in fields such as medical imaging, remote sensing, and multimedia storage, highlighting its versatility. Our findings suggest that the novel representation combined with DCT is highly effective, offering substantial storage space savings while maintaining image quality. These results are significant for theoretical research and practical implementations, providing a foundation for future advancements in image compression technologies.

Recent Applications of Cloud Computing in Medical Imaging: Advances in Medical Image Analysis and Storage of Medical Imaging Data

Recent Applications of Cloud Computing in Medical Imaging: Advances in Medical Image Analysis and Storage of Medical Imaging Data

Data Format Standardization and DICOM Integration for Hyperpolarized 13C MRI.

Hyperpolarized (HP) 13C MRI has shown promise as a valuable modality for in vivo measurements of metabolism and is currently in human trials at 15 research sites worldwide. With this growth, it is important to adopt standardized data storage practices as it will allow sites to meaningfully compare data. In this paper, we (1) describe data that we believe should be stored and (2) demonstrate pipelines and methods that utilize the Digital Imaging and Communications in Medicine (DICOM) standard. This includes proposing a set of minimum set of information that is specific to HP 13C MRI studies. We then show where the majority of these can be fit into existing DICOM attributes, primarily via the "Contrast/Bolus" module. We also demonstrate pipelines for utilizing DICOM for HP 13C MRI. DICOM is the most common standard for clinical medical image storage and provides the flexibility to accommodate the unique aspects of HP 13C MRI, including the HP agent information but also spectroscopic and metabolite dimensions. The pipelines shown include creating DICOM objects for studies on human and animal imaging systems with various pulse sequences. We also show a python-based method to efficiently modify DICOM objects to incorporate the unique HP 13C MRI information that is not captured by existing pipelines. Moreover, we propose best practices for HP 13C MRI data storage that will support future multi-site trials, research studies, and technical developments of this imaging technique.

Fast and Lightweight Image Cryptosystem for IoMT Applications

This paper presents a fast and lightweight image encryption technique founded on a new concept of progressive and selective diffusion. We make use of modified chaotic systems like the sine-logistic system, sine-tangent system, and tent-logistic system to generate key blocks for diffusion. The encryption procedure comprises three stages: column diffusion, permutation, and row diffusion, which collectively contribute to the overall encryption mechanism. In the first stage, progressive and selective column substitution of the input image is conducted employing key-blocks D1 and D2, in a manner that each substituted column data is a function of previously diffused data. In the second stage, the column-substituted image goes through an adaptive permutation operation realized using an improved sin-logistic system. The last stage incorporates two distinct mappings based on key arrays and key blocks to ensure selective and progressive inter-row substitution/diffusion. The experimental results suggest that the proposed image system exhibits rapid performance as it reports an average encryption time of 0.03 seconds for an image of the size 256×256. Besides, the use of 1-D chaotic maps together with simple yet effective confusion and diffusion processes make it lightweight. Furthermore, it provides better security performance shown by various parameters like Information Entropy Analysis (IE), Correlation Coefficient analysis (CC), Histogram analysis, Differential Attacks Analysis (NPCR and UACI), key sensitivity assessment, and fidelity analysis. The suggested system has the capability to meet the cryptographic objectives as it reports average values of IE = 7.9974, CC =0.0035, NPCR =99.61%, UACI = 33.48%, and an enormously large key space compared to the state-of-art. The proposed technique offers enhanced data security, preserving patient privacy and preventing unauthorized access, while ensuring efficient encryption performance to assure the security and immediate transmission and storage of medical images. The better security performance together with computational efficiency and lightweight nature makes it an ideal candidate to be used in IoMT systems.

A DEEP LEARNING TECHNIQUE FOR EFFICIENT MULTIMEDIA FOR DATA COMPRESSION

Medical image compression plays a pivotal role in efficient data storage and transmission, crucial for modern healthcare systems. This research proposes a convolutional transfer learning technique scheme tailored for multimedia data compression, specifically targeting medical images. In the background, the growing volume of medical imaging data and the demand for efficient storage and transmission underscore the need for innovative compression methods. Leveraging transfer learning from pre-trained convolutional neural networks (CNNs) designed for image recognition tasks, our methodology optimizes the compression process for medical images. The proposed scheme utilizes a pre-trained CNN’s feature extraction capabilities to capture relevant patterns in medical images, followed by fine-tuning on a specialized dataset. This approach capitalizes on the inherent ability of CNNs to learn hierarchical representations, enhancing the compression model’s adaptability to medical imaging nuances. The contribution of this research lies in the development of a tailored transfer learning scheme that effectively balances generic feature extraction and domain-specific adaptation for medical images. Results demonstrate significant improvements in compression efficiency, preserving diagnostic information while achieving substantial data reduction. The proposed scheme showcases promise for enhancing medical image storage, transmission, and retrieval systems, contributing to the advancement of healthcare technology.

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

<abstract> <p>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.</p> </abstract>

PCA-Chain: A Novel Medical Image Retrieval Blockchain

For decades data security has remained a challenging task for researchers. The unrivaled immutability of blockchain data and the decentralized nature of its ledger have been put forward as potential solutions to the issue. Blockchain has been proven to be effective in securely storing textual data, however, it is unable to store image files. Researchers are now focusing on implementing blockchain for storing and securing image data as images contain a large amount of sensitive data and are prone to data tampering attacks. The proposed PCA-Chain is a novel approach to providing a secure image- based blockchain using Principal Component Analysis (PCA) to compress digital leukemia images and propagate them on blockchain via simple hash functions and retrieve them back to their original size without major compression losses. Performance evaluation was conducted using MSE, PSNR and SSIMs performance parameters. PCA-Chain provides lossless compression and can be used for the storage of medical images.

Reversibly selective encryption for medical images based on coupled chaotic maps and steganography

The security and confidentiality of medical images are of utmost importance due to frequent issues such as leakage, theft, and tampering during transmission and storage, which seriously impact patient privacy. Traditional encryption techniques applied to entire images have proven to be ineffective in guaranteeing timely encryption and preserving the privacy of organ regions separated from the background. In response, this study proposes a specialized and efficient local image encryption algorithm for the medical field. The proposed encryption algorithm focuses on the regions of interest (ROI) within massive medical images. Initially, the Laplacian of Gaussian operator and the outer boundary tracking algorithm are employed to extract the binary image and achieve ROI edge extraction. Subsequently, the image is divided into ROI and ROB (regions outside ROI). The ROI is transformed into a row vector and rearranged using the Lorenz hyperchaotic system. The rearranged sequence is XOR with the random sequence generated by the Henon chaotic map. Next, the encrypted sequence is arranged according to the location of the ROI region and recombined with the unencrypted ROB to obtain the complete encrypted image. Finally, the least significant bit algorithm controlled by the key is used to embed binary image into the encrypted image to ensure lossless decryption of the medical images. Experimental verification demonstrates that the proposed selective encryption algorithm for massive medical images offers relatively ideal security and higher encryption efficiency. This algorithm addresses the privacy concerns and challenges faced in the medical field and contributes to the secure transmission and storage of massive medical images.

Protect medical records by using blockchain technology

<span>Recently, medical records have been stored and shared locally, which creates a risk of data loss or corruption; these records may be stored and shared through cloud-based central data centers; nonetheless, this strategy has disadvantages, such as the need for significant storage space and the privacy concerns associated with network-wide data sharing. Medical records require increased security and confidentiality, furthermore, these records must be protected when transmitted to and shared with doctors of the same specialization, to solve this problem, blockchain technology was used as a decentralized technology that offers a secure and immutable way to store and protect information. In this paper, a website is designed that uses blockchain technology to save medical records and employs smart contract technology in the ethereum blockchain to govern the creation and display of files and sharing. An interplanetary file system was used to offer a mechanism for the decentralized storage of medical images and reports while maintaining their accessibility on a global scale, it is available only for doctors who have the authority to access them, and the proposed system proved efficient in saving and sharing medical records with high security and less cost.</span>

9/7 LIFT Reconfigurable Architecture Implementation for Image Authentication

Considering the information system medical images are the most sensitive and critical types of data. Transferring medical images over the internet requires the use of authentication algorithms that are resistant to attacks. Another aspect is confidentiality for secure storage and transfer of medical images. The proposed study presents an embedding technique to improve the security of medical images. As a part of preprocessing that involves removing the high-frequency components, Gaussian filters are used. To get LL band features CDF9/7 wavelet is employed. In a similar way, for the cover image, the LL band features are obtained. In order to get the 1st level of encryption the technique of alpha blending is used. It combines the LL band features of the secret image and cover images whereas LH, HL, and HH bands are applied to Inverse CDF 9/7. The resulting encrypted image along with the key obtained through LH, HL, and HH bands is transferred. The produced key adds an extra layer of protection, and similarly, the receiver does the reverse action to acquire the original secret image. The PSNR acquired from the suggested technique is compared to PSNR obtained from existing techniques to validate the results. Performance is quantified in terms of PSNR. A Spartan 6 FPGA board is used to synthesize the complete architecture in order to compare hardware consumption.

A Proposed Deep-Learning-Based Framework for Medical Image Communication, Storage and Diagnosis

A Proposed Deep-Learning-Based Framework for Medical Image Communication, Storage and Diagnosis

Review on Medical Image Compression

In today’s digital era, the demand for digital medical images is rapidly increasing. Hospitals are transitioning to filmless imaging systems, emphasizing the need for efficient storage and seamless transmission of medical images. To meet these requirements, medical image compression becomes essential. However, medical image compression typically necessitates lossless compression techniques to preserve the diagnostic quality and integrity of the images. There are several challenges associated with medical image compression and management. Firstly, medical image management and image data mining involve organizing and accessing large volumes of medical images efficiently for clinical and research purposes. Secondly, bioimaging, which encompasses various imaging modalities like microscopy and molecular imaging, presents specific requirements and challenges for compression algorithms. Thirdly, virtual reality technologies are increasingly utilized in medical visualizations, demanding efficient compression methods to handle the high resolution and immersive nature of VR medical imaging data. Lastly, neuro imaging deals with complex brain imaging data, requiring specialized compression techniques tailored to the unique characteristics of these images. As the amount of medical image data continues to grow, image processing and visualization algorithms have to be adapted to handle the increased workload. Researchers and developers have been working on various compression algorithms to address these challenges and optimize medical image compression. This review paper compares different compression algorithms that would provide valuable insights into the strengths, limitations, and performance metrics of various techniques. It would assist researchers, clinicians, and imaging professionals in selecting the most suitable compression algorithm for their specific needs, considering factors such as compression ratio, computational complexity, and image quality preservation. By comprehensively comparing compression algorithms, this review paper contributes to advancing the field of medical image compression, facilitating efficient image storage, transmission, and analysis in healthcare settings.

An end-to-end screen shooting resilient blind watermarking scheme for medical images

The rapid progress in artificial intelligence and network communication has led to the development of advanced regional medical systems, revolutionizing the way hospitals and patients connect. However, the security of these systems is threatened by the potential breach of medical data, especially sensitive medical images. Existing watermarking schemes have focused on optimizing either imperceptibility or robustness, creating a fundamental trade-off between these two aspects that poses a significant challenge in the field. To address this challenge and ensure the confidentiality and integrity of medical data, this paper proposes an end-to-end screen shooting resistant blind watermarking scheme for medical images. The proposed method offers a robust and reliable solution for the secure transmission and storage of medical images, particularly in the face of screen shooting attacks. To enhance both security and imperceptibility, the scheme employs a novel encryption technique based on BCH codes, resulting in a trinary sequence for watermark images. These watermarks are then embedded in the spatial domain, effectively protecting against unauthorized access while preserving the integrity of the original image. To further enhance robustness, the method introduces a neural network structure specifically designed to resilient screen shooting attacks. By simulating the screen shooting process and incorporating various distortions, this network combines the strengths of residual networks and generative adversarial networks. Moreover, the proposed method introduces a novel loss function that exploits the unique characteristics of the image transformation domain, optimizing both imperceptibility and robustness. In the subsequent experiment, the watermarked images achieved satisfactory results with the highest PSNR and SSIM values reaching 62.563 dB and 0.9999, respectively. The proposed scheme provides an effective solution to protect the privacy and copyright of medical images, effectively addressing a critical security concern prevalent in regional healthcare systems.

Efficient reconfigurable architecture to enhance medical image security

Medical images are one of the most critical and sensitive types of data in information systems. For the secure storage and transfer of medical images, confidentiality is the most important aspect. This paper presents efficient embedding technique to enhance medical image security. The Gaussian filters are used as preprocessing to remove high frequency components and then applied to cumulative distribution function (CDF) 5/3 wavelet to obtain LL band features. Similarly, the LL band features of cover image are obtained. The alpha bending technique combines both the LL band features of cover and secret image to form first level of encryption and now with other high frequency bands of LH, HL and HH applied to Inverse CDF 5/3 obtains an encrypted image which is then transferred along with key obtained through other bands of LH, HL and HH. The key generated acts as additional level of security and similarly, at the receiver the opposite operations are performed to obtain the original secrete image. The performance is measured in terms of Peak signal-to-noise ratio (PSNR) and is compared with existing techniques to validate the results. Further, the entire architecture is synthesized on spartan 6 field-programmable gate array (FPGA) board to compare the hardware utilizations.

Classification of medical X-ray images using supervised and unsupervised learning approaches

Most of the traditional approaches for medical image storage are least capable and scanning of relevant matching images are quite difficult. The existing approaches of content-based image retrieval (C-BIR) are less focused with medical images. The available research works with fuzzy logic approaches are very less and not efficient for medical image retrieval. Thus, there is a need of research work that can address both supervised and unsupervised learning approaches for medical image retrieval. Hence, the C-BIR technique is evolved with overcoming above stated concerns. Hence, this manuscript introduces two different C-BIR techniques using a support vector machine (SVM) and a fuzzy logic-based approach for classification. These approaches work on the classification based on feature extraction, region of Interest (ROI), corner detection, and similarity matching. The proposed approach has been analyzed for image retrieval for accuracy. The outcomes of the proposed study enhance the classification performances with retrieval than existing techniques of C-BIR.

A Novel Framework for Securing ECDH Encrypted DICOM Pixel Data Stored Over Cloud Using IPFS

The future holds the possibility of hospitals sharing medical images obtained through non-invasive systems to patients remotely. The advent of cloud and the storage and deployment of medical healthcare images in the cloud has resulted in the increased need for application of Cryptographic techniques to protect them from unauthorized access and malicious attacks. The Digital Imaging and Communication in Medicine (DICOM) standard is more compatible across medical imaging instruments globally. The pixel data of DICOM images requires more privacy and security. A novel ECDS based cryptographic approach is suggested to encrypt the original DICOM image as well as the ROI pixel data extracted from DICOM images. Results computed experimentally have proved that medical image encryption via ECDH is more robust, efficient and faster than existing medical image encryption schemes.