水平集(1evel set)图像分割方法是图像分割中的一个重要方法，但是该算法的计算量大，往往不能达到实时处理的要求。给出了利用新一代的可编程图形处理器(GPU)实现level set的加速算法。首先介绍了如何在GPU上利用片元渲染程序进行网格化的线性运算和有限差分PDE计算，把level set方法的离散化算子映射到GPU上。由于以数据流处理方式的GPU的存储访问快，具有并行运算能力，同时level set算法演化的显示不再需要把数据从CPU传到GPU，因此较大地提高了算法速度与交互显示。文中实现并测试了一个与初始化状态独立的二维level set的算子用于图像分割，并对其运算结果和性能进行了比较，结果表明该方法具有更快的速度。

Level set methods are powerful tool to segment images, but these algorithms have a large computational burden thus are not suitable for real time processing requirement. In this paper, an new accelerating algorithm of level set method is presented which is implemented on the new generation of graphics processing unit(GPU) instead of on CPU. It first introduced how to implement grid computation for algebraic linear operation and finite difference solution of PDE on GPU by fragment program, and then map the level set solver to GPU. Since GPU is a parallel vector processor for streamed data with big bandwidth for data access, and the result data don't need to be transferred from CPU to GPU for data rendering, so the accelerating algorithm is suitable for real time processing and rendering. In this paper a 2D level set solver for image segmentation was tested with comparison of the performance result between fast marching method and the GPU accelerated method. It shows this method can achieve 60 percent quicker.