Image segmentation using CUDA implementations of the Runge-Kutta-Merson and GMRES methods
Department of Mathematics, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University
Journal of Math-for-Industry, Volume 3(C), p73-79, 2012
@article{oberhuber2012image,
title={Image segmentation using CUDA implementations of the Runge-Kutta-Merson and GMRES methods},
author={Oberhuber, T. and Suzuki, A. and Vacata, J. and {v{Z}}abka, V.},
journal={Journal of Math-for-Industry},
volume={3},
number={C},
pages={73–79},
year={2012}
}
Modern GPUs are well suited for performing image processing tasks. We utilize their high computational performance and memory bandwidth for image segmentation purposes. We segment cardiac MRI data by means of numerical solution of an anisotropic partial differential equation of the Allen-Cahn type. We implement two different algorithms for solving the equation on the CUDA architecture. One of them is based on the Runge-Kutta-Merson method for the approximation of solutions of ordinary differential equations, the other uses the GMRES method for the numerical solution of systems of linear equations. In our experiments, the CUDA implementations of both algorithms are about 3-9 times faster than corresponding 12-threaded OpenMP implementations.
February 22, 2012 by hgpu
