146 From Grid to Healthgrid T. Solomonides et al. (Eds.) IOS Press, 2005 © 2005 The authors. All rights reserved. Parametric Optimization of a Model-Based Segmentation Algorithm for Cardiac MR Image Analysis: A Grid-Computing Approach S. ORDAS a,1 , H.C. VAN ASSEN b , J. PUENTE c , B.P.F. LELIEVELDT b and A.F. FRANGI a a Computational Imaging Laboratory, Universitat Pompeu Fabra, Barcelona, Spain b Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands c GridSystems, S.A., Mallorca, Spain Abstract. In this work we present a Grid-based optimization approach performed on a set of parameters that affects both the geometric and grey-level appearance properties of a three-dimensional model-based algorithm for cardiac MRI segmen- tation. The search for optimal values was assessed by a Monte Carlo procedure us- ing computational Grid technology. A series of segmentation runs were conducted on an evaluation database comprising 30 studies at two phases of the cardiac cycle (60 datasets), using three shape models constructed by different methods. For each of these model-patient combinations, six parameters were optimized in two steps: those which affect the grey-level properties of the algorithm first and those relating to the geometrical properties, secondly. Two post-processing tasks (one for each stage) collected and processed (in total) more than 70000 retrieved result files. Qualitative and quantitative validation of the fitting results indicates that the seg- mentation performance was greatly improved with the tuning. Based on the ex- perienced benefits with the use of our middleware, and foreseeing the advent of large-scale tests and applications in cardiovascular imaging, we strongly believe that the use of Grid computing technology in medical image analysis constitutes a real necessity. Keywords. Parametric optimization, Grid computing, Statistical model-based segmentation, MRI analysis Introduction In the last few years, many model-based approaches for image segmentation have con- tributed to the quite evolving field of medical image analysis. The rationale behind these methods is to analyze the image in a top-down fashion: a generic template model of the structure of interest is subsequently instantiated and deformed to accommodate for the clues provided by image information. For a cardiac application, it is possible to 1 Correspondence to: Sebastián Ordás, Computational Imaging Laboratory, Department of Technology, Universitat Pompeu Fabra, Passeig de Circumval.lacio 8, E08003 Barcelona, Spain. e-mail: sebastian.ordas@upf.edu.