Storage Of Medical Images Research Articles

Medical Image Encryption Algorithm based on Dynamic DNA Sequencing and 2D-3D Chaotic Equations

Digital images are now widely used in modern clinic diagnosis. The diagnostic images with confidential information related to patients’ privacy are stored and transmitted via public networks. Secured schemes to guarantee confidentiality of patients’ privacy are becoming more and more vital. Medical images are considered as one of the most important and sensitive data in information systems. Sending medical images over the network requires a strong encryption algorithm such that it is resistant against cryptographic attacks. Among the three security objectives for the security of information systems namely confidentiality, integrity and availability, confidentiality is the most important aspect that need to be taken much care for the secure storage and transmission of medical images. This paper proposes an adaptive medical image encryption algorithm based on Dynamic DNA Sequencing and 2D-3D Chaotic Equations that encrypts any medical image with same uniformity and efficiency.

Server-Aided 3D DICOM Viewer for Mobile Platforms

Digital Imaging and Communications in Medicine (DICOM) is an international standard to transfer, store, retrieve, print, process and display medical imaging information. It provides a standardized method to store medical images from many types of imaging devices. Typically, CT and MRI scans, which are composed of 2D slice images in DICOM format, can be inspected and analyzed with DICOM-compatible imaging software. Additionally, the DICOM format provides important information to assemble cross-sections into 3D volumetric datasets. Not many DICOM viewers are available for mobile platforms (smartphones and tablets), and most of them are 2D-based with limited functionality and user interaction. This paper reports on our efforts to design and implement a volumetric 3D DICOM viewer for mobile devices with real-time rendering, interaction, a full transfer function editor and server access capabilities. 3D DICOM image sets, either loaded from the device or downloaded from a remote server, can be rendered at up to 60 fps on Android devices. By connecting to our server, users can a) get pre-computed image quality metrics and organ segmentation results, and b) share their experience and synchronize views with other users on different platforms.

A hybrid encryption scheme based on optical scanning cryptography and Fibonacci–Lucas transformation

This paper describes a new opto-hybrid technique of two-stage encryption for the secure transmission and reception of sensitive information contained in the form of images and data. An advanced encryption–decryption technique based on optical scanning holography (optical scanning cryptography) and Fibonacci–Lucas transformation is proposed. The first stage of this hybrid system includes a point spread function engineered optical scanning cryptographic system. A new key based on fused biometric array is used in this stage. A digital encryption strategy follows this stage. This hybrid encryption scheme can be used for the secure transfer and storage of medical images in the Internet, especially in cloud-based services. The quantitative analysis is performed using different figure of merits. The quantitative investigations have been conducted through computer simulations. The parameters analyzed for establishing the performance characteristics of this system are Structural Similarity Index, Correlation Coefficient, Maximum Absolute Deviation, etc. This system shows a remarkable performance improvement during encryption. The method can be explicitly used in the radiograph image transfer over the Internet for telemedicine applications.

Fast Reaching Finite Time synchronization Approach for Chaotic Systems With Application in Medical Image Encryption

This article proposes a fast reaching finite time synchronization approach for chaotic systems along with its application to medical image encryption. First, an adaptive terminal sliding mode tracking approach with fast reaching condition is designed to synchronize the chaotic systems at the transmitter and receiver ends in finite time. Then, a chaotic cryptosystem, using synchronized chaotic systems as secret keys generator, is proposed to enhance the security of medical image transmission and/or storage. The applicability and efficiency of the proposed synchronization approach is assessed using a simulation as well as an analytical study. The analysis encompassed security tools such as histogram analysis, correlation test, and information entropy change the rate of the number of pixels and unified average changing intensity. The obtained results confirmed the robustness and fast convergence rate of the proposed synchronization approach. The security analysis also shows that the proposed cryptosystem displays acceptable levels of resistance to various attacks.

A hybrid medical image coding based on block truncation coding and residual vector quantisation

The advancement of medical field has witnessed tremendous growth with state of the art imaging technologies for accurate diagnosis, which in turn demands efficient storage of medical images. Further enhance the image quality RVQ is implemented in the proposed method. The proposed work aims at developing an effective algorithm for compressing medical images exploiting the advantages of block truncation coding (BTC) and residual vector quantisation (RVQ). The advantage of the block truncation coding (BTC) is twofold: 1) it is simple to implement; 2) involves less computational complexity. RVQ is used to enhance the quality further. In the proposed method, the input image is compressed using BTC in the first phase. The residual error out of first phase is subjected to RVQ in the second phase. The novel feature of the proposed method is the adaptive procedure of RVQ based on the variance of residual vectors. High variant residual vectors are subject to vector quantisation and low variant vectors to scalar quantisation respectively. Furthermore, the residual values are normalised to positive values, so as to preserve their sign, before quantisation. Experimental results show the superior performance of the proposed method in terms of compression metrics.

Blockchain-Based Distributed Patient-Centric Image Management System

In recent years, many researchers have focused on developing a feasible solution for storing and exchanging medical images in the field of health care. Current practices are deployed on cloud-based centralized data centers, which increase maintenance costs, require massive storage space, and raise privacy concerns about sharing information over a network. Therefore, it is important to design a framework to enable sharing and storing of big medical data efficiently within a trustless environment. In the present paper, we propose a novel proof-of-concept design for a distributed patient-centric image management (PCIM) system that is aimed to ensure safety and control of patient private data without using a centralized infrastructure. In this system, we employed an emerging Ethereum blockchain and a distributed file system technology called Inter-Planetary File System (IPFS). Then, we implemented an Ethereum smart contract called the patient-centric access control protocol to enable a distributed and trustworthy access control policy. IPFS provides the means for decentralized storage of medical images with global accessibility. We describe how the PCIM system architecture facilitates the distributed and secured patient-centric data access across multiple entities such as hospitals, patients, and image requestors. Finally, we deployed a smart contract prototype on an Ethereum testnet blockchain and evaluated the proposed framework within the Windows environment. The evaluation results demonstrated that the proposed scheme is efficient and feasible.

Hybrid Chaotic Method for Medical Images Ciphering

Healthcare is an essential application of e-services, where for diagnostic testing, medical imaging acquiring, processing, analysis, storage, and protection are used. Image ciphering during storage and transmission over the networks used has seen implemented using many types of ciphering algorithms for security purpose. Current cyphering algorithms are classified into two types: traditional classical cryptography using standard algorithms (DES, AES, IDEA, RC5, RSA, ...) and chaos cryptography using continuous (Chau, Rossler, Lorenz, ...) or discreet (Logistics, Henon, ...) algorithms. The traditional algorithms have struggled to combat image data as compared to regular textual data. Whereas, the chaotic algorithms are more efficient for image ciphering. The Significancecharacteristics of chaos are its extreme sensitivity to initial conditions and algorithm parameters. In this paper, medical image security based on hybrid/mixed chaotic algorithms is proposed. The proposed method is implemented using MATLAB. Where the image of the Retina of the Eye to detect Blood Vessels is ciphered. The Pseudo-Random Numbers Generators (PRNGs) from the different chaotic algorithms are implemented, and their statistical properties are evaluated using the National Institute of Standards and Technology NIST and other statistical test-suits. Then, these algorithms are used to secure the data, where the statistical properties of the cipher-text are also tested. We propose two PRNGs to increase the complexity of the PRNGs and to allow many of the NIST statistical tests to be passed: one based on twohybrid mixed chaotic logistic maps and one based on two-hybrid mixed chaotic Henon maps, where each chaotic algorithm runs side-by-side andstarts with random initial conditions and parameters (encryption keys). The resulting hybrid PRNGs passed many of the NIST statistical test suits.

Gaussian Hermite polynomial based lossless medical image compression

The role of compression is inevitable in the storage and transmission of medical images. The polynomial based image compression is proposed in this work for the compression of abdomen CT medical images. The input images are preprocessed by min–max normalization; the pixels are scanned and subjected to polynomial approximation. The polynomial approximated coefficients are subjected to llyods quantization and encoded by arithmetic coder. The medical image compression using Gaussian Hermite polynomial gives superior results when compared with the legendre polynomial based image compression and JPEG lossless compression techniques in terms of Peak to signal noise ratio (PSNR), Mean square Error (MSE) and other picture quality metrics. The algorithms are tested on real-time DICOM abdomen CT image and can be used for data transfer in teleradiology application.

Development of a Cloud Database for Storage and Processing of Medical Images

We present here the development of a cloud-based solution for storing and processing medical data obtained using TMR-KFTI MRI systems developed at the Kazan Physical-Technical Institute, FRC Kazan Scientific Center, Russian Academy of Sciences, and devices from other manufacturers. The concept of cloud technologies is considered, and their advantages over conventional data storage methods are identified. Types of cloud storage are investigated, and examples from world practice are presented; the grounds for a specific solution are established. The potentials for the development of cloud technologies in medicine are addressed.

A Cloud Solution for Securing Medical Image Storage

Cloud computing is an easy-to-use, affordable solution to manage and analyze medical data. Therefore, this paradigm has gained wide acceptance in the healthcare sector as a cost-efficient way for a successful Electronic Medical Records (EMR) implementation. Cloud technology is, however, subject to increasing criticism because of the numerous security vulnerabilities. In this regard, we propose a framework to protect confidential data through the development of new security measures, including compression, secret share scheme and XOR operation. The primary objective of the proposal is to achieve the right balance between security and usability. To this aim, we divide an image into several blocks and then encrypt each piece separately with different cryptographic keys. To enhance privacy and performance, we suggest DepSky architecture to keep data on various storage nodes. Simulation experiments have been conducted to prove the effectiveness of the proposed methodology.

RETRACTED ARTICLE: A secure medical image transmission algorithm based on binary bits and Arnold map

The responsibility of maintaining patient’s records is with medical personnel. The medical personnel are supposed not to disclose any kind of medical information related to the patient. This is also applicable for the medical information discovered by medical personnel in connection with the treatment of the patient. With the advent of technology and its penetration into the medical field in the form of telemedicine and e-health, the challenge of maintaining confidentiality is becoming complex. The confidentiality needs to be protected from storage of medical image or transmission of image from a medical database center to other. Nowadays, the information of patient is printed in the corner of the medical image. This can be accessed by anybody or even the machine can access and store the information. During an electronic transmission, the patient information may be intercepted by a third party. This may lead to big lawsuit. Apart from these security issues, for scenarios such as medical research, the image should be used but patient information should be hidden. Also for diagnostic purposes, the information of the patient should be readily accessible to the medical personnel. These constraints lead to the necessity of suitable security techniques for medical image storage and transmission. If suitable security techniques are not applied, privacy of patients will be stake. Hence, numerous methods are being implemented by individuals, governments and businesses for secured transmission of patient’s data. For patient’s privacy protection, secured transmission requires techniques like cryptography and watermarking. These techniques achieve confidentiality and integrity. In this work, a new approach has been developed for secured transmission of medical images.

Research on Medical Image Storage and Retrieval System Based on Hadoop

With the Continuous Improvement of Medical Information, the Continuous Development of Image Processing Technology and the Continuous Upgrading of Hospital Image Acquisition Equipment, Medical Image Detection Has Become More and More Reliable and Effective, Becoming an Important Basis for Doctors’ Diagnosis. Now Large Hospitals Use the Combination of Radiology Information Management System and Image Archiving and Communication System to Store and Access Patients’ Images. with the Establishment of Regional Image Sharing and Cooperation Platform, the Online - Near Line - Offline Storage Strategy Adopted by the Traditional Image Archiving and Communication System Can No Longer Meet the Needs of Mass Image Data Storage. in the Retrieval of Mass Image Data, the Relational Database Used by the Image Archiving and Communication System is Inefficient in Reading the Image on the Scale of Mass Data, and the Existing Image the Archive and Communication System Lacks Complete Data Backup and Disaster Recovery Scheme. This Paper Analyses the Feasibility, Advantages and Problems to Be Solved of Using Hdfs to Store Massive Medical Image Files, and Studies How to Realize the Function of Precise Access and Range Access Provided by Pacs Efficiently in Hadoop.

Storage and Security Issues of Medical Images using Cloud Platform

In this current era of technology people are interested more in online medical facilities for more faster and comfortable life. Because of huge demand, health care field is growing rapidly. The data in the form of x-rays, diagnosis reports, MRI images, videos is generated. Such form of data consumes large amount of storage space and processing power, therefore nowadays alternate solutions such as cloud storage are used to store such huge data. As the medical data in the form of images or videos is very important and confidential it is very crucial to concentrate on the issues like storage and security. The proposed work discuss about the image security techniques like watermarking using different types combined with data encryption techniques during transmission so as to ensure better security along with reduction in cost for storage with the use of cloud environment.In this study, we offer a deeper insight into the challenges hindering the adoption of this technology. Then we analyze and compare these findings of the cloud based medical image process implementation with security necessities.

Blockchain Technology: Principles and Applications in Medical Imaging.

Blockchain is an immutable, encrypted, distributed ledger technology. While initially devised for and most commonly referenced with cryptocurrencies, there are an increasing number of applications outside finance, many of which are relevant to medical imaging. In this paper, the concepts and principles underlying the technology and applications relevant to medical imaging are discussed, in addition to potential challenges with implementations such as public versus private key access, distributed ledger size constraints, speed, complexity, and security pitfalls. Potential use cases for blockchain specifically relevant to medical imaging include image sharing including direct patient ownership of images, tracking of implanted medical devices, research, teleradiology, and artificial intelligence. While blockchain offers exciting ways to facilitate the storage and distribution of medical images, similar to the advent of picture archiving and communication systems decades ago, it does have several key limitations of which healthcare providers of medical imaging and imaging informatics professionals should be aware.

HYBRID CHAOTIC METHOD FOR MEDICAL IMAGES CIPHERING

Healthcare is an essential application of e-services, where for diagnostic testing, medical imaging acquiring, processing, analysis, storage, and protection are used. Image ciphering during storage and transmission over the networks used has seen implemented using many types of ciphering algorithms for security purpose. Current cyphering algorithms are classified into two types: traditional classical cryptography using standard algorithms and chaos cryptography using continuous or discreet algorithms. The traditional algorithms have struggled to combat image data as compared to regular textual data. Whereas, the chaotic algorithms are more efficient for image ciphering. The Significance characteristics of chaos are its extreme sensitivity to initial conditions and algorithm parameters. In this paper, medical image security based on hybrid/mixed chaotic algorithms is proposed. The proposed method is implemented using MATLAB. Where the image of the Retina of the Eye to detect Blood Vessels is ciphered. The Pseudo-Random Numbers Generators (PRNGs) from the different chaotic algorithms are implemented, and their statistical properties are evaluated using the National Institute of Standards and Technology NIST and other statistical test-suits. Then, these algorithms are used to secure the data, where the statistical properties of the cipher-text are also tested. We propose two PRNGs to increase the complexity of the PRNGs and to allow many of the NIST statistical tests to be passed: one based on two-hybrid mixed chaotic logistic maps and one based on two-hybrid mixed chaotic Henon maps, where each chaotic algorithm runs side-by-side and starts with random initial conditions and parameters (encryption keys). The resulting hybrid PRNGs passed many of the NIST statistical test suits.

Особенности распределенного хранения медицинских изображений в онкологической службе в рамках создания единого цифрового контура

The development of an effective unified information space of the regional radiology diagnostics service is possible by connecting all outpatient cancer care centers and medical organizations to distributed medical image storage systems under the management of regional radiological information systems within the framework of the regional programs “Creation of the unified digital circuit in healthcare based on the unified state health information system” of the territorial subjects of the Russian Federation. The aim of the research is to study the features of using radiological information systems in the oncological profile and develop recommendations for creating distributed archives of medical images in the oncology service within the framework of the federal project. The paper considers the results of the implementation of software products “LINS LookInside”, “LINS Makhaon” in the framework of implementation of local and regional projects in cancer clinics. The data on the connected diagnostic equipment and integration with medical information systems are provided. It has been established that each medical organization with an oncological profile should have its own local PACS installed. Using the example of federal cancer centers, it is recommended to include endoscopic departments, radionuclide and ultrasound diagnostics rooms in radiological information systems, as well as to install and connect systems for automatic publication of CD/DVD discs with DICOM studies.

An Efficient E2C2 Visual Cryptographic Technique to Secure Medical Images in Cloud Environment

Secure storage and easy retrieval of data is major concern for patients when medical data is stored over cloud platform. The unique features of cloud environment are high performance, data availability and security among which security of data stored in cloud environment is sensitive it can be easily attacked by intruders. Concerning medical images, while storing data, proper secure authentication method should be applied in order to avoid misuse of medical data. When this medical image is retrieved, the clarity of retrieved image should be high in order to determine the exact cause of sickness. This paper focuses on proposing a secure storage and retrieval of medical images stored in cloud with visual cryptographic technique using AES algorithm. The experimental results show that the proposed system is able to store images securely and retrieve medical images with high clarity.

A novel weighted compressive sensing using L1-magic recovery technique in medical image compression.

Recent technological advancement in computing technology, communication systems, and machine learning techniques provides opportunities to biomedical engineers to achieve the requirements of clinical practice. This requires storage and/or transmission of medical images with the conservation of the medical information over the communication channel. Accordingly, medical compression is necessary for efficient channel bandwidth utilization. To solve the trade-off between the compression ratio and the preservation of significant information, compressed sensing (CS) can be used. During image recovery in CS, an optimization algorithm is used, such as greedy pursuit, convex relaxation, and Bayesian framework. In the present work, a convex relaxation optimization called L1-magic is employed, where the objective function can be relaxed to the nearest convex norm, i.e., ℓ1-norm. In addition, the discrete cosine transform is used for recovery by transforming the image from time- to frequency-domain. To improve the medical image recovery, a weighted L1-magic is proposed using a threshold based on the image content, where high weight is given to the significant details in the image. Thus, the significant information in the image (values greater than the threshold) is multiplied by a weight factor according to the image characteristics for a successful recovery process. A comparative study of the proposed weighted L1-magic and orthogonal matching pursuit (OMP), one of the greedy algorithms, was conducted. Different metrics were measured, including the Structural Similarity Index Measure and Peak Signal-to-Noise Ratio (PSNR) to evaluate CS performance using the proposed weighted L1-magic as well as the weighted OMP and the principal component analysis (PCA) as a traditional compression method at different compression ratios (CR). The experimental results on diabetic retinopathy images dataset proved the superiority of the weighted L1-magic method, where for example as 0.4 CR, average PSNR is 19.37, 17.95, and 15.64 using the weighted L1-magic, weighted OMP, and PCA, respectively.

Imaging Examination and Quantitative Detection and Analysis of Gastrointestinal Diseases Based on Data Mining Technology.

The medical image storage and transmission system completes the collection, storage, management, diagnosis and information processing of digital medical image information generated from digital medical devices, accumulates a large amount of data resources, and uses these valuable data resources to extract corresponding diseases. Diagnostic rules that help improve the accuracy of clinical disease diagnosis have always been the subject of medical research and management. Gastrointestinal diseases are common high-risk digestive diseases. This paper studies the imaging detection and quantitative detection and analysis of gastrointestinal diseases based on data mining, aiming to improve the accuracy of doctors' clinical diagnosis, reduce the misdiagnosis and misdiagnosis of patients' diseases, and reduce the burden on patients. With the high computing speed and computational accuracy of the computer, combined with the flexible analysis and judgment ability of the human body, the doctor can help the semi-structured and unstructured diagnosis problems. Experiments demonstrate the effectiveness and robustness of the proposed method.

MISS-D: A fast and scalable framework of medical image storage service based on distributed file system

Background and Objective Processing of medical imaging big data is deeply challenging due to the size of data, computational complexity, security storage and inherent privacy issues. Traditional picture archiving and communication system, which is an imaging technology used in the healthcare industry, generally uses centralized high performance disk storage arrays in the practical solutions. The existing storage solutions are not suitable for the diverse range of medical imaging big data that needs to be stored reliably and accessed in a timely manner. The economical solution is emerging as the cloud computing which provides scalability, elasticity, performance and better managing cost. Cloud based storage architecture for medical imaging big data has attracted more and more attention in industry and academia.Methods This study presents a novel, fast and scalable framework of medical image storage service based on distributed file system. Two innovations of the framework are introduced in this paper. An integrated medical imaging content indexing file model for large-scale image sequence is designed to adapt to the high performance storage efficiency on distributed file system. A virtual file pooling technology is proposed, which uses the memory-mapped file method to achieve an efficient data reading process and provides the data swapping strategy in the pool.Result The experiments show that the framework not only has comparable performance of reading and writing files which meets requirements in real-time application domain, but also bings greater convenience for clinical system developers by multiple client accessing types. The framework supports different user client types through the unified micro-service interfaces which basically meet the needs of clinical system development especially for online applications. The experimental results demonstrate the framework can meet the needs of real-time data access as well as traditional picture archiving and communication system.Conclusions This framework aims to allow rapid data accessing for massive medical images, which can be demonstrated by the online web client for MISS-D framework implemented in this paper for real-time data interaction. The framework also provides a substantial subset of features to existing open-source and commercial alternatives, which has a wide range of potential applications.