摘要 :
It is a common belief that the shift to digital imaging some 20 years ago helped medical image exchange and got rid of any potential image loss that was happening with printed image films. Unfortunately, this is not the case: desp...
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It is a common belief that the shift to digital imaging some 20 years ago helped medical image exchange and got rid of any potential image loss that was happening with printed image films. Unfortunately, this is not the case: despite the most recent advances in digital imaging, most hospitals still keep losing their imaging data, with these losses going completely unnoticed. As a result, not only does image loss affect the faith in digital imaging but it also affects patient diagnosis and daily quality of clinical work. This paper identifies the origins of invisible image losses, provides methods and procedures to detect image loss, and demonstrates modes of action that can be taken to stop the problem from happening.
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Digital imaging and communication in medicine (DICOM) specifies that all DICOM objects have globally unique identifiers (UIDs). Creating these UIDs can be a difficult task due to the variety of techniques in use and the requiremen...
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Digital imaging and communication in medicine (DICOM) specifies that all DICOM objects have globally unique identifiers (UIDs). Creating these UIDs can be a difficult task due to the variety of techniques in use and the requirement to ensure global uniqueness. We present a simple technique of combining a root organization identifier, assigned descriptive identifiers, and JAVA generated unique identifiers to construct DICOM compliant UIDs.
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DicomWorks is freeware software for reading and working on medical images [digital imaging and communication in medicine (DICOM)]. It was jointly developed by two research laboratories, with the feedback of more than 35,000 regist...
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DicomWorks is freeware software for reading and working on medical images [digital imaging and communication in medicine (DICOM)]. It was jointly developed by two research laboratories, with the feedback of more than 35,000 registered users throughout the world who provided information to guide its development. We detail their occupations (50% radiologists, 20% engineers, 9% medical physicists, 7% cardiologists, 6% neurologists, and 8% others), geographic origins, and main interests in the software. The viewer’s interface is similar to that of a picture archiving and communication system viewing station. It provides basic but efficient tools for opening DICOM images and reviewing and exporting them to teaching files or digital presentations. E-mail, FTP, or DICOM protocols are supported for transmitting images through a local network or the Internet. Thanks to its wide compatibility, a localized (15 languages) and user-friendly interface, and its opened architecture, DicomWorks helps quick development of non proprietary, low-cost image review or teleradiology solutions in developed and emerging countries.
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Clinical picture archiving and communications systems provide convenient, efficient access to digital medical images from multiple modalities but can prove challenging to deploy, configure and use. MRIdb is a self-contained image ...
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Clinical picture archiving and communications systems provide convenient, efficient access to digital medical images from multiple modalities but can prove challenging to deploy, configure and use. MRIdb is a self-contained image database, particularly suited to the storage and management of magnetic resonance imaging data sets for population phenotyping. It integrates a mature image archival system with an intuitive web-based user interface that provides visualisation and export functionality. In addition, utilities for auditing, data migration and system monitoring are included in a virtual machine image that is easily deployed with minimal configuration. The result is a freely available turnkey solution, designed to support epidemiological and imaging genetics research. It allows the management of patient data sets in a secure, scalable manner without requiring the installation of any bespoke software on end users' workstations. MRIdb is an open-source software, available for download at http://www3.imperial.ac.uk/bioinfsupport/resources/software/mridb.
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摘要 :
Clinical picture archiving and communications systems provide convenient, efficient access to digital medical images from multiple modalities but can prove challenging to deploy, configure and use. MRIdb is a self-contained image ...
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Clinical picture archiving and communications systems provide convenient, efficient access to digital medical images from multiple modalities but can prove challenging to deploy, configure and use. MRIdb is a self-contained image database, particularly suited to the storage and management of magnetic resonance imaging data sets for population phenotyping. It integrates a mature image archival system with an intuitive web-based user interface that provides visualisation and export functionality. In addition, utilities for auditing, data migration and system monitoring are included in a virtual machine image that is easily deployed with minimal configuration. The result is a freely available turnkey solution, designed to support epidemiological and imaging genetics research. It allows the management of patient data sets in a secure, scalable manner without requiring the installation of any bespoke software on end users’ workstations. MRIdb is an open-source software, available for download at http://www3.imperial.ac.uk/bioinfsupport/resources/software/mridb.
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Abstract Virtual medical imaging has overgrown in recent years and later implemented in such situations - all of the radiological modalities which include Computed tomography (CT scanners), Magnetic resonance imaging (MRI), ultras...
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Abstract Virtual medical imaging has overgrown in recent years and later implemented in such situations - all of the radiological modalities which include Computed tomography (CT scanners), Magnetic resonance imaging (MRI), ultrasound (US), positron emission tomography (PET), X-Ray (radiographs) made employing more than one providers and hosted by one or many websites which communicated over the DICOM network. The DICOM images required huge hard disk space and excellent transfer speed, which need to compress the DICOM images for effective capacity and transmission over the internet. The compression process is applied through utilized a recurrent neural network algorithm establishing Trainscg as the activation function. Extraordinary high-quality metrics like mean squared error (MSE), Peak signal-to-noise ratio (PSNR), Compression ratio (CR), and Compression time are computed on several medical test images. The proposed compression method shows better experimental results than the existing techniques based on performance parameters except for the compression time for the large image only.
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Digital Imaging and Communications in Medicine (DICOM) is the dominant standard for medical imaging data. DICOM-compliant devices and the data they produce are generally designed for clinical use and often do not match the needs o...
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Digital Imaging and Communications in Medicine (DICOM) is the dominant standard for medical imaging data. DICOM-compliant devices and the data they produce are generally designed for clinical use and often do not match the needs of users in research or clinical trial settings. DicomBrowser is software designed to ease the transition between clinically oriented DICOM tools and the specialized workflows of research imaging. It supports interactive loading and viewing of DICOM images and metadata across multiple studies and provides a rich and flexible system for modifying DICOM metadata. Users can make ad hoc changes in a graphical user interface, write metadata modification scripts for batch operations, use partly automated methods that guide users to modify specific attributes, or combine any of these approaches. DicomBrowser can save modified objects as local files or send them to a DICOM storage service using the C-STORE network protocol. DicomBrowser is open-source software, available for download at http://nrg.wustl.edu/software/dicom-browser.
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A methodology to automatically detect potential retakes in digital imaging, using the Digital Imaging and Communications in Medicine (DICOM) header information, is presented. In our hospital, neither the computed radiography works...
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A methodology to automatically detect potential retakes in digital imaging, using the Digital Imaging and Communications in Medicine (DICOM) header information, is presented. In our hospital, neither the computed radiography workstations nor the picture archiving and communication system itself are designed to support reject analysis. A system called QCOnline, initially developed to help in the management of images and patient doses in a digital radiology department, has been used to identify those images with the same patient identification number, same modality, description, projection, date, cassette orientation, and image comments. The pilot experience lead to 6.6% and 1.9% repetition rates for abdomen and chest images. A thorough analysis has shown that the real repetitions were 3.3% and 0.9% for abdomen and chest images being the main cause of the discrepancy being the wrong image identification. The presented methodology to automatically detect potential retakes in digital imaging using DICOM header information is feasible and allows to detect deficiencies in the department performance like wrong identifications, positioning errors, wrong radiographic technique, bad image processing, equipment malfunctions, artefacts, etc. In addition, retake images automatically collected can be used for continuous training of the staff.
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With the modernization of biomedical image equipment and image processing technology, the amount of medical image data increases rapidly. These medical images need to be transmitted over a public network between doctors or hospita...
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With the modernization of biomedical image equipment and image processing technology, the amount of medical image data increases rapidly. These medical images need to be transmitted over a public network between doctors or hospitals to provide telemedicine services and sometimes need to be uploaded to a telemedicine data center or health cloud due to limited local computing and storage resources. Medical images involve confidential and sensitive information of patients, and the confidentiality technology to protect patient privacy is becoming more and more important. These problems have been studied by many scholars, but these studies are largely limited and scattered. To the authors' knowledge, there is no systematic literature review related to medical image confidentiality technology. To provide valuable insights into the technical environment and to support researchers, the available options and gaps in this research area must be understood. Therefore, in this study, the recent 5 years' research results in the field of medical image security are reviewed to map the field of study into a coherent taxonomy. We use five major academic databases which are (1) Medline, (2) IEEE Xplore, (3) WoS, (4) Scopus, and (5) ScienceDirect, and every literature related to (1) medical images, and (2) encryption were searched with a focus in these databases. These databases contain literature about medical image confidentiality technology. According to the classification scheme, the final data set consists of 123 pieces of literature, and are divided into five categories. The first category, which also occupies the most proportion, focuses on medical image confidentiality algorithms, and most of them adopt chaotic mapping correlative technology The second category considers multiple security requirements while studying medical image confidentiality. The third category combines medical image encryption and image compression technologies. The fourth category focuses on the hardware related medical image confidentiality design. The final category is the medical image security systems design. We then identify the basic characteristics of this emerging field from the following aspects; the motivations of using medical image confidentiality technology, the challenges of medical image confidentiality technology, and the recommendations for further research and use of medical image confidentiality technologies. Finally, the quality evaluation criteria of the medical image security algorithm are summarized, including but not limited to the evaluation of security and the evaluation of speed and time complexity. The results of the reference evaluation show that most studies on medical image security algorithms lack sufficient safety evaluation. This was evident from less than 30% of the total articles that used all kinds of security evaluation methods.
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