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In this article we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned in subsets to be processed on different grid nodes. A theoretical model of the application computation cost and estimates of the grid execution overhead are used to efficiently partition the database. We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time.
The availability of digital imagers inside hospitals and their ever growing inspection capabilities have established digital medical images as a key component of many pathologies diagnosis, follow-up and treatment. To face the growing image analysis requirements, automated medical image processing algorithms have been developed over the two past decades. In parallel, medical image databases have been set up in health centers. Some attempts have been made to cross data coming from different origins for studies involving large databases. Grid technologies appear to be a promising tool to face the raising challenges of computational medicine. They offer wide area access to distributed databases in a secured environment and they bring the computational power needed to complete some large scale statistical studies involving image processing...
The deployment of biomedical applications in a grid environment has started about three years ago in several European projects and national ini-tiatives. These applications have demonstrated that the grid paradigm was rele-vant to the needs of the biomedical community. They have also highlighted that this community had very specific requirements on middleware and needed fur-ther structuring in large collaborations in order to participate to the deployment of grid infrastructures in the coming years. In this paper, we propose several ar-eas where grid technology can today improve research and healthcare. A cru-cial issue is to maximize the cross fertilization among projects in the perspec-tive of an environment where data of medical interest can be stored and made easily available to the different actors of healthcare, the physicians, t...
This paper describes the effort to deploy a Medical Data Management service on top of the EGEE grid infrastructure. The most widely accepted medical image stan- dard, DICOM, was developed for fulfilling clinical practice. It is implemented in most medical image acquisition and analysis devices. The EGEE middleware is us- ing the SRM standard for handling grid files. Our prototype is exposing an SRM compliant interface to the grid middleware, transforming on the fly SRM requests into DICOM transactions. The prototype ensures user identification, strict file ac- cess control and data protection through the use of relevant grid services. This Medical Data Manager is easing the access to medical databases needed for many medical data analysis applications deployed today. It offers a high level data man- agement service, compatible with cli...
Computation and data grids have encountered a large success among the scientific computing community in the past few years. The medical imaging community is increasingly aware of the potential benefit of these technologies in facing today medical image analysis challenges. In this paper, we report on a first experiment in deploying a medical application on a large scale grid testbed. Our pilot application is a hybrid metadata and image content-based query system that manipulates a large data set and for which image analysis computation can be easily parallelized on several grid nodes. We analyze the performances of this algorithm and the benefit brought by the grid. We further discuss possible improvements and future trends in porting medical applications to grid infrastructures.
Grids have emerged as a promising technology to handle the data and compute intensive requirements of many application areas. Digital medical image processing is a promising application area for grids. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. The research project AGIR (Grid Analysis of Radiological Data) presented in this paper addresses this challenge through a combined approach: on one hand, leveraging the grid middleware through core grid medical services which target the requirements of medical data processing applications; on the other hand, grid-enabling a panel of applications ranging from algorithmic resea...
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