Grid-enabling medical image analysis

Cécile Germain-Renaud (1) , Vincent Breton (2) , Patrick Clarysse (3) , Yann Gaudeau (4) , Tristan Glatard (5) , Emmanuel Jeannot (6) , Yannick Legre (2) , Charles Loomis (7) , Isabelle Magnin (3) , Johan Montagnat (5) , Jean-Marie Moureaux (4) , Angel Osorio (8) , Xavier Pennec (8) , Romain Texier (1)

1 LRI - Laboratoire de Recherche en Informatique
2 LPC - Laboratoire de Physique Corpusculaire - Clermont-Ferrand
3 CREATIS - Centre de Recherche et d'Application en Traitement de l'Image et du Signal
4 CRAN - Centre de Recherche en Automatique de Nancy
5 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis
6 ALGORILLE - Algorithms for the Grid
7 LAL - Laboratoire de l'Accélérateur Linéaire
8 ASCLEPIOS - Analysis and Simulation of Biomedical Images

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 research to clinical applications.

Domaines: Calcul parallèle, distribué et partagé [cs.DC]

Cécile Germain-Renaud, Vincent Breton, Patrick Clarysse, Yann Gaudeau, Tristan Glatard, et al.. Grid-enabling medical image analysis. International Journal of Clinical Monitoring and Computing, 2005, 19 (4-5), pp.339-349. ⟨10.1007/s10877-005-0679-9⟩. ⟨inria-00174284⟩