43 research outputs found
Grid Databases for Shared Image Analysis in the MammoGrid Project
The MammoGrid project aims to prove that Grid infrastructures can be used for
collaborative clinical analysis of database-resident but geographically
distributed medical images. This requires: a) the provision of a
clinician-facing front-end workstation and b) the ability to service real-world
clinician queries across a distributed and federated database. The MammoGrid
project will prove the viability of the Grid by harnessing its power to enable
radiologists from geographically dispersed hospitals to share standardized
mammograms, to compare diagnoses (with and without computer aided detection of
tumours) and to perform sophisticated epidemiological studies across national
boundaries. This paper outlines the approach taken in MammoGrid to seamlessly
connect radiologist workstations across a Grid using an "information
infrastructure" and a DICOM-compliant object model residing in multiple
distributed data stores in Italy and the UKComment: 10 pages, 5 figure
Experiences of Engineering Grid-Based Medical Software
Objectives: Grid-based technologies are emerging as potential solutions for
managing and collaborating distributed resources in the biomedical domain. Few
examples exist, however, of successful implementations of Grid-enabled medical
systems and even fewer have been deployed for evaluation in practice. The
objective of this paper is to evaluate the use in clinical practice of a
Grid-based imaging prototype and to establish directions for engineering future
medical Grid developments and their subsequent deployment. Method: The
MammoGrid project has deployed a prototype system for clinicians using the Grid
as its information infrastructure. To assist in the specification of the system
requirements (and for the first time in healthgrid applications), use-case
modelling has been carried out in close collaboration with clinicians and
radiologists who had no prior experience of this modelling technique. A
critical qualitative and, where possible, quantitative analysis of the
MammoGrid prototype is presented leading to a set of recommendations from the
delivery of the first deployed Grid-based medical imaging application. Results:
We report critically on the application of software engineering techniques in
the specification and implementation of the MammoGrid project and show that
use-case modelling is a suitable vehicle for representing medical requirements
and for communicating effectively with the clinical community. This paper also
discusses the practical advantages and limitations of applying the Grid to
real-life clinical applications and presents the consequent lessons learned.Comment: 18 pages, 2 tables, 5 figures. In press International Journal of
Medical Informatics. Elsevier publisher
A formal architecture-centric and model driven approach for the engineering of science gateways
From n-Tier client/server applications, to more complex academic Grids, or even the most recent and promising industrial Clouds, the last decade has witnessed significant developments in distributed computing. In spite of this conceptual heterogeneity, Service-Oriented Architecture (SOA) seems to have emerged as the common and underlying abstraction paradigm, even though different standards and technologies are applied across application domains. Suitable access to data and algorithms resident in SOAs via so-called âScience Gatewaysâ has thus become a pressing need in order to realize the benefits of distributed computing infrastructures.In an attempt to inform service-oriented systems design and developments in Grid-based biomedical research infrastructures, the applicant has consolidated work from three complementary experiences in European projects, which have developed and deployed large-scale production quality infrastructures and more recently Science Gateways to support research in breast cancer, pediatric diseases and neurodegenerative pathologies respectively. In analyzing the requirements from these biomedical applications the applicant was able to elaborate on commonly faced issues in Grid development and deployment, while proposing an adapted and extensible engineering framework. Grids implement a number of protocols, applications, standards and attempt to virtualize and harmonize accesses to them. Most Grid implementations therefore are instantiated as superposed software layers, often resulting in a low quality of services and quality of applications, thus making design and development increasingly complex, and rendering classical software engineering approaches unsuitable for Grid developments.The applicant proposes the application of a formal Model-Driven Engineering (MDE) approach to service-oriented developments, making it possible to define Grid-based architectures and Science Gateways that satisfy quality of service requirements, execution platform and distribution criteria at design time. An novel investigation is thus presented on the applicability of the resulting grid MDE (gMDE) to specific examples and conclusions are drawn on the benefits of this approach and its possible application to other areas, in particular that of Distributed Computing Infrastructures (DCI) interoperability, Science Gateways and Cloud architectures developments
Image processing methods and architectures in diagnostic pathology.
Grid technology has enabled the clustering and the efficient and secure access to and interaction among a wide variety of geographically distributed resources such as: supercomputers, storage systems, data sources, instruments and special devices and services. Their main applications include large-scale computational and data intensive problems in science and engineering. General grid structures and methodologies for both software and hardware in image analysis for virtual tissue-based diagnosis has been considered in this paper. This methods are focus on the user level middleware. The article describes the distributed programming system developed by the authors for virtual slide analysis in diagnostic pathology. The system supports different image analysis operations commonly done in anatomical pathology and it takes into account secured aspects and specialized infrastructures with high level services designed to meet application requirements. Grids are likely to have a deep impact on health related applications, and therefore they seem to be suitable for tissue-based diagnosis too. The implemented system is a joint application that mixes both Web and Grid Service Architecture around a distributed architecture for image processing. It has shown to be a successful solution to analyze a big and heterogeneous group of histological images under architecture of massively parallel processors using message passing and non-shared memory
The Impact of Grid on Health Care Digital Repositories
Grid computing has attracted worldwide attention in a variety of applications like Health Care. In this paper we identified the Grid services that could facilitate the integration and interoperation of Health Care data and frameworks world-wide. While many of the current Health Care Grid projects address issues such as data location and description on the Grid and the security aspects, the problems connected to data storage, integrity, preservation and distribution have been neglected. We describe the currently available Grid storage services and protocols that can come in handy when dealing with those problems. We further describe a Grid infrastructure to build a cooperative Health Care environment based on currently available Grid services and a service able to validate it
Federating distributed and heterogeneous information sources in neuroimaging: the NeuroBase Project.
The NeuroBase project aims at studying the requirements for federating, through the Internet, information sources in neuroimaging. These sources are distributed in different experimental sites, hospitals or research centers in cognitive neurosciences, and contain heterogeneous data and image processing programs. More precisely, this project consists in creating of a shared ontology, suitable for supporting various neuroimaging applications, and a computer architecture for accessing and sharing relevant distributed information. We briefly describe the semantic model and report in more details the architecture we chose, based on a media-tor/wrapper approach. To give a flavor of the future deployment of our architecture, we de-scribe a demonstrator that implements the comparison of distributed image processing tools applied to distributed neuroimaging data