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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...
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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