Digital image management networks: current status.
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Computer Communication Networks↗
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Results for “Digital network”
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Impact of digital miniaturization and networked topologies on access to next generation telecommunication by people with visual disabilities.
In the past, telecommunication technologies did not present any particular problem for persons with visual disabilities. The telephones themselves were auditory in nature and could be operated by touch. As telecommunication begins to incorporate video and as telecommunication devices become more complex (including the incorporation of visual displays), new barriers are appearing. Fortunately, advancing technologies are also providing new opportunities for access. The rapidly shrinking size and cost of electronics is allowing us to build intelligence and flexibility into telecommunication products. Advances will soon allow voice to be incorporated into most devices. In addition, clever use of networks and network-based services will allow access features to be built directly into the network, providing access to key visual information. As a result, future telecommunication systems can be more accessible than technologies of the past-if they are implemented correctly.
Communication Devices for People with Disabilities↗
PACS. Adopting an all-digital filmless imaging network.
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California↗
Digital dyslexia. Neural network mimics the effects of stroke.
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Brain↗
Digital echocardiographic laboratory: where do we stand?
The transition to an all-digital echocardiographic laboratory has been slow despite the advantages of digital echocardiography, the dramatic improvement in computer technology, and the acceptance by all major vendors and professional organizations of the Digital Imaging and Communications in Medicine image-formatting standard. This review article examines some current issues in digital echocardiography, including types of digital output, disk versus network exchange, digital and clinical compression techniques, and the choice of image storage format. Although specific exceptions exist, we conclude that the optimal solution will be one based on a network exchange of formatted images that follow the Digital Imaging and Communications in Medicine standard, now available from several manufacturers. For a clinical interpretation, modest compression with the Joint Photography Expert Group algorithm appears acceptable, though possible enhancements to this compression standard are also discussed. We hope this review will enable the echocardiographic community to make more intelligent choices as digital storage and transmission products become available in the marketplace.
Algorithms↗
Communication networks for medical image transmission.
Digital communication networks are of increasing importance for data exchange in health care environments. They may be used to transmit multi-media data, such as text, images, graphics, signals and sound. The essential characteristics of modern network technologies are summarized in this article and are seen in the context of local, metropolitan and wide area networks. Standardized technologies discussed are Ethernet, token oriented systems, FDDI, DQDB and ATM. Off-line communication media based on magnetic optical disk, such as IS&C, are briefly introduced. The conclusion reached is that therapy planning for radiation therapy or hyperthermia can make use of communication technologies, for example, to transmit patient images, modelling data and results of distribution calculations of physical phenomena.
Computer Communication Networks↗
Update in bioinformatics. Toward a digital database of plant cell signalling networks: advantages, limitations and predictive aspects of the digital model.
The process of signal integration, which contributes to the regulation of multiple cellular activities, can be described in a digital language by a set of connected digital operations. In this article we delineate the basic concepts of cell signalling in the context of a logical description of information processing. Newly described instances of signal integration in plants are given as examples. The different advantages, limitations and predictive aspects of the digital modeling of signal transduction networks, as well as the minimal architecture of a computer database for plant signalling networks are discussed.
Computational Biology↗
DigiNet: Optimizing personalized care for patients with stage IV non-small cell lung cancer (NSCLC) through a digitally connected provider network-analysis plan of a prospective multicenter cohort trial.
PURPOSE: The German sector-based healthcare system poses a major challenge to continuous patient monitoring and long-term follow-up, both essential for generating high-quality, longitudinal real-world data. The national Network for Genomic Medicine (nNGM) bridges the inpatient and outpatient care sectors to provide comprehensive molecular diagnostics and personalized treatment for non-small cell lung cancer (NSCLC) patients in Germany. Building on the established nNGM infrastructure, the DigiNet study aims to evaluate the impact of digitally integrated, personalized care on overall survival (OS) and the optimization of treatment pathways, compared to routine care. METHODS: DigiNet is a prospective, controlled, non-randomized multicenter cohort study including patients with stage IV NSCLC in two study regions (East and West) in Germany. The results of molecular diagnostics and clinical information, along with the entire treatment data are documented in a shared database. A board of lung cancer specialists monitors critical events. Patients digitally complete quality of life questionnaires, with results visualized for physicians. To assess the impact of this personalized digital care, a population-based control group will be identified by matching cohorts within the involved cancer registries. The primary endpoint is OS, and secondary endpoints comprise time on first-line treatment and hospitalization rates. Furthermore, a health economic and business economic evaluation will be conducted. Qualitative interviews with patients and physicians will be performed to assess barriers and facilitating factors for implementing the DigiNet intervention. ETHICS: The study protocol was reviewed and approved by the Ethics Committee of the University Hospital of Cologne (21-1521). TRIAL REGISTRATION: NCT05818449, registered retrospectively on December 12, 2022.
Humans↗
Identification of rice seed varieties using neural network.
A digital image analysis algorithm based color and morphological features was developed to identify the six varieties (ey7954, syz3, xs11, xy5968, xy9308, z903) rice seeds which are widely planted in Zhejiang Province. Seven color and fourteen morphological features were used for discriminant analysis. Two hundred and forty kernels used as the training data set and sixty kernels as the test data set in the neural network used to identify rice seed varieties. When the model was tested on the test data set, the identification accuracies were 90.00%, 88.00%, 95.00%, 82.00%, 74.00%, 80.00% for ey7954, syz3, xs11, xy5968, xy9308, z903 respectively.
Algorithms↗
[Reading screening mammograms with the help of neural networks].
With digital mammography it is possible to assist radiologists in breast cancer screening with computers to improve their reading performance. The need for this has been demonstrated by studies showing a large variability in skill of radiologists reading mammograms. Moreover, retrospective studies show that a significant number of cancers are clearly visible on earlier screening mammograms, even for 'trained' computers. Methods for automated detection of breast cancer in mammograms often use artificial neural networks. These are 'trained' to recognize abnormal mammographic areas using a large database of known cases. For detection of microcalcification clusters very reliable algorithms exist, with such high sensitivity that radiologists can limit their search to areas that have been marked 'suspect' by the computer. The development of methods to recognize malignant masses is much more difficult, but ample progress has been achieved in recent years.
Adult↗
Coordination of fingertip forces during human manipulation can emerge from independent neural networks controlling each engaged digit.
We investigated the coordination of fingertip forces in subjects who lifted an object (i) using the index finger and thumb of their right hand, (ii) using their left and right index fingers, and (iii) cooperatively with another subject using the right index finger. The forces applied normal and tangential to the two parallel grip surfaces of the test object and the vertical movement of the object were recorded. The friction between the object and the digits was varied independently at each surface between blocks of trials by changing the materials covering the grip surfaces. The object's weight and surface materials were held constant across consecutive trials. The performance was remarkably similar whether the task was shared by two subjects or carried out unimanually or bimanually by a single subject. The local friction was the main factor determining the normal:tangential force ratio employed at each digit-object interface. Irrespective of grasp configuration, the subjects adapted the force ratios to the local frictional conditions such that they maintained adequate safety margins against slips at each of the engaged digits during the various phases of the lifting task. Importantly, the observed force adjustments were not obligatory mechanical consequences of the task. In all three grasp configurations an incidental slip at one of the digits elicited a normal force increase at both engaged digits such that the normal:tangential force ratio was restored at the non-slipping digit and increased at the slipping digit. The initial development of the fingertip forces prior to object lift-off revealed that the subjects employed digit-specific anticipatory mechanisms using weight and frictional experiences in the previous trial. Because grasp stability was accomplished in a similar manner whether the task was carried out by one subject or cooperatively by two subjects, it was concluded that anticipatory adjustments of the fingertip forces can emerge from the action of anatomically independent neural networks controlling each engaged digit. In contrast, important aspects of the temporal coordination of the digits was organized by a "higher level" sensory-based control that influenced both digits. In lifts by single subjects this control was mast probably based on tactile and visual input and on communication between neural control mechanisms associated with each digit. In the two-subject grasp configuration this synchronization information was based on auditory and visual cues.
Adolescent↗
Estimating digital information throughput rates for radiology networks. A model.
The design and implementation of a digital radiology image management system requires the definition, evaluation, and comparison of appropriate measures of system performance. The mean throughput rate is an important measure of the actual performance of a finished system. The mean throughput rate identifies the transmission of digital information either in bits/second or tasks/second. It is dependent on software, database management, equipment interface designs, number of users and display stations, and communications media. The mean throughput rate can document resource allocation bottlenecks within a given system. A model for estimating the mean throughput rate and its application in helping us design our radiology digital image networks is described.
Electronic Data Processing↗
CASANDRA: a prototype implementation of a system of network progressive transmission of medical digital images.
In this paper, a prototype for progressive transmission of medical digital 2D images through the network, called CASANDRA, is presented. The prototype consists of the server part and the client part. In the server part, the images are acquired, stored, computed their wavelet transform and the wavelet coefficients stored, then transmitted progressively, when required, via TCP to the client. In the client part, with the inverse wavelet transform, the received wavelet coefficients are used to build successive improved reconstructions of the image. This prototype has been implemented and is being tested in the Radiotherapy Service of the Valencia University Hospital (Valencia, Spain).
Medical Informatics Applications↗
[Successful media exchange and network communication of CT digital image].
The digital image standard in medical is discussed. Based on the DICOM standard by using the media exchange and establishing the network communication between PC and CT system, we successfully acquired the CT original digital image and convert it to DICOM standard image. The CT image can easitly be read and adjusted on the PC platform.
Computer Communication Networks↗
Initial experience with a radiology imaging network to newborn and intensive care units.
A digital image network has been installed in the James Whitcomb Riley Hospital for Children on the Indiana University Medical Center to create a limited all digital imaging system. The system is composed of commercial components, Philips/AT&T CommView system, (Philips Medical Systems, Shelton, CT; AT&T Bell Laboratories, West Long Beach, NJ) and connects an existing Philips Computed Radiology (PCR) system to two remote workstations that reside in the intensive care unit and the newborn nursery. The purpose of the system is to display images obtained from the PCR system on the remote workstations for direct viewing by referring clinicians, and to reduce many of their visits to the radiology reading room three floors away. The design criteria includes the ability to centrally control all image management functions on the remote workstations to relieve the clinicians from any image management tasks except for recalling patient images. The principal components of the system are the Philips PCR system, the acquisition module (AM), and the PCR interface to the Data Management Module (DMM). Connected to the DMM are an Enhanced Graphics Display Workstation (EGDW), an optical disk drive, and a network gateway to an ethernet link. The ethernet network is the connection to the two Results Viewing Stations (RVS) and both RVSs are approximately 100 m from the gateway. The DMM acts as an image file server and an image archive device. The DMM manages the image data base and can load images to the EGDW and the two RVSs. The system has met the initial design specifications and can successfully capture images from the PCR and direct them to the RVSs.(ABSTRACT TRUNCATED AT 250 WORDS)
Computer Communication Networks↗
Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines.
OBJECTIVE: Detection and characterization of microcalcification clusters in mammograms is vital in daily clinical practice. The scope of this work is to present a novel computer-based automated method for the characterization of microcalcification clusters in digitized mammograms. METHODS AND MATERIAL: The proposed method has been implemented in three stages: (a) the cluster detection stage to identify clusters of microcalcifications, (b) the feature extraction stage to compute the important features of each cluster and (c) the classification stage, which provides with the final characterization. In the classification stage, a rule-based system, an artificial neural network (ANN) and a support vector machine (SVM) have been implemented and evaluated using receiver operating characteristic (ROC) analysis. The proposed method was evaluated using the Nijmegen and Mammographic Image Analysis Society (MIAS) mammographic databases. The original feature set was enhanced by the addition of four rule-based features. RESULTS AND CONCLUSIONS: In the case of Nijmegen dataset, the performance of the SVM was Az=0.79 and 0.77 for the original and enhanced feature set, respectively, while for the MIAS dataset the corresponding characterization scores were Az=0.81 and 0.80. Utilizing neural network classification methodology, the corresponding performance for the Nijmegen dataset was Az=0.70 and 0.76 while for the MIAS dataset it was Az=0.73 and 0.78. Although the obtained high classification performance can be successfully applied to microcalcification clusters characterization, further studies must be carried out for the clinical evaluation of the system using larger datasets. The use of additional features originating either from the image itself (such as cluster location and orientation) or from the patient data may further improve the diagnostic value of the system.
Breast Diseases↗
Dental identification using digital images via computer network.
Dental identification is a useful scientific method. In Japan, however, there are only a few forensic odontologists; moreover, until now, forensic dental services have only been offered by general dentists. These dentists may not be able to offer such forensic services during office time. For a quick comparison, the authors tried sending digital photos, taken with a 2-million-pixel digital camera, to dental offices via the Internet. If a dental office has Internet access, it is possible for dental charting to be sent directly to the autopsy room. Of course, digital images only provide the first outline. However, when antemortem dental records of the person in question are available at autopsy, a quick comparison can be made.
Adult↗
Analog-to-digital clinical data collection on networked workstations with graphic user interface.
An innovative respiratory examination system has been developed that combines physiological response measurement, real-time graphic displays, user-driven operating sequences, and networked file archiving and review into a scientific research and clinical diagnosis tool. This newly constructed computer network is being used to enhance the research center's ability to perform patient pulmonary function examinations. Respiratory data are simultaneously acquired and graphically presented during patient breathing maneuvers and rapidly transformed into graphic and numeric reports, suitable for statistical analysis or database access. The environment consists of the hardware (Macintosh computer, MacADIOS converters, analog amplifiers), the software (HyperCard v2.0, HyperTalk, XCMDs), and the network (AppleTalk, fileservers, printers) as building blocks for data acquisition, analysis, editing, and storage. System operation modules include: Calibration, Examination, Reports, On-line Help Library, Graphic/Data Editing, and Network Storage.
Analog-Digital Conversion↗