Institute for Data Analysis and Visualization

Level set methods are a powerful tool for implicitly representing deformable surfaces. Since their inception, these techniques have been used to solve problems in fields as varied as computer vision, scientific visualization, computer graphics and computational physics. With the power and flexibility of this approach; however, comes a large computational burden. In the level set approach, surface motion is computed via a partial differential equation (PDE) framework. One possibility for accelerating level-set based applications is to map the solver kernel onto a commodity graphics processing unit (GPU). GPUs are parallel, vector computers whose power is currently increasing at a faster rate than that of CPUs. in this work, we demonstrate a GPU-based, three-dimensional level set solver that is capable of computing curvature flow as well as other speed terms. Results are shown for this solver segmenting the brain surface from an MRI data set.