Medical image registration algorithms assessment: Bronze Standard application enactment on grids using the MOTEUR workflow engine

Medical image registration is pre-processing needed for many medical image analysis procedures. A very large number of registration algorithms are available today, but their performance is often not known and very difficult to assess due to the lack of gold standard. The Bronze Standard algorithm is a very data and compute intensive statistical approach for quantifying registration algorithms accuracy. In this paper, we describe the Bronze Standard application and we discuss the need for grids to tackle such computations on medical image databases. We demonstrate MOTEUR, a service-based workflow engine optimized for dealing with data intensive applications. MOTEUR eases the enactment of the Bronze Standard and similar applications on the EGEE production grid infrastructure. It is a generic workflow engine, based on current standards and freely available, that can be used to instrument legacy application code at low cost. 1 The Bronze Standard application Computerized medical image analysis is now a well established area that provides assistance for diagnosis, modeling, and pathologies follow-up. With the growing inspection capabilities of imagers and the medical data production growth, the need for large amounts of data storage and computing power increases. Grids have been identified as a tool suitable for dealing with medical data. Successful example of grid application deployment for image databases analysis, optimization of medical image algorithms, simulation, etc, have already been reported [7]. 1.1 Medical images registration Medical image registration algorithms are playing a key role in a very large number of medical image analysis procedures. Together with image segmentation algorithms, they are fundamental processings often needed prior to any subsequent analysis. Image registration consists in searching a 3D transformation between two images, so that the first one can superimpose on the second one in a common 3D frame. The transformation may be rigid (the composition of a translation and a rotation) to express a 3D change of frame or non rigid to express local deformations of space. A rigid registration is useful for aligning similar data (such as images of a same patient acquired at different times) into a single frame. A non-rigid registration is useful for computing the deformation map between different data (such as data acquired from two different patients). In addition, the registration