基于CUDA的快速三维医学图像分割  

Fast 3D Medical Image Segmentation Based on CUDA

作　　者：孟晓林[1] 秦安[1] 徐建[1] 陈武凡[1] 冯前进[1] 

机构地区：[1]南方医科大学生物医学工程学院,广东广州510515

出　　处：《中国医学物理学杂志》2010年第2期1716-1720,共5页Chinese Journal of Medical Physics

基　　金：广东省产学研重点项目(No.cgzhzd0714)

摘　　要：目的:三维分割是医学图像分析和可视化中的重要组成部分,也是医学图像分割中的一个难点。水平集方法在三维医学图像分割中有很广阔的应用前景,但是该算法的计算量大,不能达到实时处理的要求。针对这个问题,提出了一种基于CUDA的并行加速方法。方法:采用NVIDIA公司的GPGPU模型CUDA,利用图像像素的独立性和偏微分方程求解的并发性,提高C-V水平集算法的分割速度。给出了并行计算的流程图,并对C-V水平集算法在CUDA上的实现进行了详细介绍。结果:实现了C-V水平集并行加速算法,该方法在保证分割效果的前提下,具有更快的分割速度。结论:所提出的方法是切实可行的,实现了快速的三维医学图像分割。Objective： 3D segmentation is an important part of medical image analysis and visualization. It also continues to be large challenge in the medical image segmentation. While level sets have demonstrated a great potential for 3D medical image segmentation, these algorithms have a large computational burden thus are not suitable for real time processing requirement. To solve this problem, we propose a parallel aeeerelated method based on CUDA. Methods： We implement C-V level set algorithm in the CUDA environment which is the NVIDIA＇s GPGPU model.The segmentation speed can greatly improved by using independence of image pixel and concurrence of partial differential equation .The paper shows the flow chart of the parallel computing and gives the detailed introduction of the C-V level set algorithm which is implemented in the CUDA environment. Results： Realizing the C-V level set parallel accerelated algorithm. This method has faster segmentation speed while preserving the qualitative results. Conclusions： This method is viable and makes the fast 3D medical image segmentation come true.

关 键 词：水平集 医学图像分割 CUDA 并行图像处理 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]