Content-based organisation of virtual repositories of DICOM objects

Espert, Ignacio Blanquer; García, Vicente Hernández; Anastásio, Fco. Javier Meseguer; Quilis, J. Damiàn Segrelles

doi:10.1016/j.future.2008.12.004

Cited by 18 publications

(6 citation statements)

References 13 publications

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“…The TRENCADIS medical data manager [11] for instance is a WSRF-compliant middleware designed for sharing, searching and processing medical images and structured radiological reports. It is one of the first examples of a fully integrated grid system taking into account native medical image interfaces, security constraints and medical ontologies.…”

Section: Related Workmentioning

confidence: 99%

A Secure Grid Medical Data Manager Interfaced to the gLite Middleware

Montagnat¹,

Frohner

Jouvenot³

et al. 2007

J Grid Computing

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Abstract. The medical community is producing and manipulating a tremendous volume of digital data for which computerized archiving, processing and analysis is needed. Grid infrastructures are promising for dealing with challenges arising in computerized medicine but the manipulation of medical data on such infrastructures faces both the problem of interconnecting medical information systems to grid middlewares and of preserving patients' privacy in a wide and distributed multiuser system. These constraints are often limiting the use of grids for manipulating sensitive medical data.This paper describes our design of a medical data management system taking advantage of the advanced gLite data management services, developed in the context of the EGEE project, to fulfill the stringent needs of the medical community. It ensures medical data protection through strict data access control, anonymization and encryption. The multi-level access control provides the flexibility needed for c 2007 Kluwer Academic Publishers. Printed in the Netherlands.GRID172.tex; 1 2 J. Montagnat, A. Frohner, D. Jouvenot, C. Pera et al implementing complex medical use-cases. Data anonymization prevents the exposure of most sensitive data to unauthorized users, and data encryption guarantees data protection even when it is stored at remote sites. Moreover, the developed prototype provides a grid Storage Resource Manager (SRM) interface to standard medical DICOM servers thereby enabling transparent access to medical data without interfering with medical practice.Keywords: Secure grid storage, gLite middleware, medical data management 1. Context ObjectivesMany scientific areas benefit from large and distributed storage capabilities provided by grid infrastructures. On top of physical storage resources, the EGEE [17] grid data management system eases the manipulation of large data volumes and provides high level functionality such as data distribution, replication and optimized access. To build a data management system that can adapt to the heterogeneous data storage resources (disk, tapes, silos...), the grid community has adopted standard interfaces to virtualize the underlying resources. In particular, gLite [23], the next generation EGEE middleware, has adopted the Storage Resource Manager (SRM) interface [29] standardized in the context of the Open Grid Forum [28]. The SRM's primary concern is to provide efficient access to large volumes of data. It provides, among other services, prefetching of data files recorded on secondary storage, management of storage space and reservation of storage resources. However, it does not provide any access control nor protection of data which severely limits its usability for applications manipulating sensitive data.In this paper, we address the problem of sensitive data management on the EGEE grid infrastructure and we introduce a data management service designed to handle medical records on grids. We first motivate our approach through an in-depth requirement analysis of data management in the medical ar...

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Section: Related Workmentioning

confidence: 99%

A Secure Grid Medical Data Manager Interfaced to the gLite Middleware

Montagnat¹,

Frohner

Jouvenot³

et al. 2007

J Grid Computing

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“…For instance, project CVIMO [11] has provided an e-infrastructure for health research that uses a Grid approach to securely share oncology medical images among different hospitals. For instance, project CVIMO [11] has provided an e-infrastructure for health research that uses a Grid approach to securely share oncology medical images among different hospitals.…”

Section: Introductionmentioning

confidence: 99%

“…A practical example could be a set of different centers, which hold different parts of the data. For instance, project CVIMO [11] has provided an e-infrastructure for health research that uses a Grid approach to securely share oncology medical images among different hospitals. In this context, physicians of different hospitals collaborate, improving diagnosis and treatment.…”

Section: Introductionmentioning

confidence: 99%

A replicated information system to enable dynamic collaborations in the Grid

Torres

Moltó

Segrelles

et al. 2012

Concurrency and Computation

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The main advantage of Grid computing over other distributed computing paradigms is its capability to coordinate the access to data and resources in a virtual multi-institutional environment. To this end, the information system plays a decisive role in selecting the services that meet the applications' needs. This paper presents an information system for the Grid that provides transparent and scalable group communication services to standard Grid applications, with the objective of supporting dynamic collaborations that could help address problems that involve only some participants of a virtual organization. In particular, it enables more flexible delivery mechanisms, which allows applications to select the appropriate services before sending their data to the information system. This significantly enhances the protection of data from unauthorized access, and avoids the transmission of unnecessary messages over the network. The proposed information system is based on the use of XML technologies and replication. It introduces several new advanced features that are not currently supported as a whole by any Grid middleware, such as: several entry points to the information, persistent capabilities, support for advanced queries based on XQuery, and support for the industrial standard WS-Policy. The information system has been stress tested under realistic workloads in a Grid infrastructure with 50 sites. Scalability has been evaluated in up to 1000 messages that can be up to 10 KB in size each, updated with a frequency of 5 min.Initiative is a strong collaborative and cooperative link with the Portuguese National Grid Initiative through the IBERGRID agreement. This infrastructure is connected to the European Grid Infrastructure (EGI) [6]. The IBERGRID VO for Life Science applications (life.vo.ibergrid.eu) is an example of VO that has been created in the context of EGI to support the research activity in the life science community. This VO enables international collaboration in medical imaging, bioinformatics, and drug discovery.However, dynamic collaborations present a challenge to Grid information systems, because they do not provide a simple way to define special access rights agreed by part of the members of a VO. For example, in a system like VOMS [7], this problem occurs when two or more groups of the VO agree to collaborate on the achievement of a particular objective. Although this can be tackled through the use of thematic VO subgroups, this approach has the disadvantage of requiring the approval of an administrator of the VO (except for very few services such as file catalogues), and requires the reconfiguration of the infrastructure to support differentiation among groups. Moreover, any interaction with the membership to groups must be done through the VO admin role, but even so, the usage of groups to support temporal collaborations is a widely used feature (e.g., biomed VO, IBERGRID VOs) because users in the VO are normally authorized to access all the resources, regardless of the group that they belong to...

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“…Current progress in Information Technologies allows for affordable huge data storages and powerful computational possibilities; thus, they have become quite common. Researchers are gradually becoming aware of the importance of keeping together diverse data pertaining to a specific medical entity and successful attempts to create and maintain such databases are becoming known to the scientific community [1][2][3][4]. Data models specified by standards are often included in databases, without taking into account inherent limitations posed by the procedure of acquiring original data.…”

Section: Introductionmentioning

confidence: 99%

A Software Framework for Building Biomedical Machine Learning Classifiers through Grid Computing Resources

2011

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This paper describes the BiomedTK software framework, created to perform massive explorations of machine learning classifiers configurations for biomedical data analysis over distributed Grid computing resources. BiomedTK integrates ROC analysis throughout the complete classifier construction process and enables explorations of large parameter sweeps for training third party classifiers such as artificial neural networks and support vector machines, offering the capability to harness the vast amount of computing power serviced by Grid infrastructures. In addition, it includes classifiers modified by the authors for ROC optimization and functionality to build ensemble classifiers and manipulate datasets (import/export, extract and transform data, etc.). BiomedTK was experimentally validated by training thousands of classifier configurations for representative biomedical UCI datasets reaching in little time classification levels comparable to those reported in existing literature. The comprehensive method herewith presented represents an improvement to biomedical data analysis in both methodology and potential reach of machine learning based experimentation.

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Content-based organisation of virtual repositories of DICOM objects

Cited by 18 publications

References 13 publications

A Secure Grid Medical Data Manager Interfaced to the gLite Middleware

A Secure Grid Medical Data Manager Interfaced to the gLite Middleware

A replicated information system to enable dynamic collaborations in the Grid

A Software Framework for Building Biomedical Machine Learning Classifiers through Grid Computing Resources

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