Encoding information using fewer bits than the unencoded representation of data would. Lossy compression methods do not allow to fully retrieve the original data, while lossless compression methods do.
Published in Chapter:
Grid Analysis of Radiological Data
Cecile Germain-Renaud (CNRS, France), Vincent Breton (CNRS Clermont-Ferrand, France), Patrick Clarysse (INSA-Lyon, France), Bertrand Delhay (INSA-Lyon, France), Yann Gaudeau (CNRS Strasbourg, France), Tristan Glatard (Universite de Lyon CREATIS-LRMN, France), Emmanuel Jeannot (Universite Henri Poincare, France), and Yannick Legre (Université Blaise Pascal, France)
Copyright: © 2009
|Pages: 29
DOI: 10.4018/978-1-60566-374-6.ch019
Abstract
Grid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. 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. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical use cases both exploits and drives the development of the services.