论文标题
平行的3DPIFCM算法用于嘈杂的脑MRI图像
Parallel 3DPIFCM Algorithm for Noisy Brain MRI Images
论文作者
论文摘要
在本文中，我们通过在GPU上使用CUDA在[1]中实现了[1]中称为3DPIFCM的算法。在我们以前的工作中，我们引入了3DPIFCM，该工作在嘈杂的条件下对图像进行分割，并使用粒子群优化来查找最佳算法参数来解释噪声。与FCM（模糊C-Means），IFCMPSO（改善具有粒子群优化粒子的Fuzzy c-Means）相比，该算法达到了艺术分割精度的状态。 当在单台计算机上使用遗传算法或PSO（粒子群优化）以进行优化时，我们目睹了长时间的执行时间用于实际临床用法。因此，在当前的论文中，我们的目标是通过取出算法的一部分并将其作为GPU上的内核来加快执行3DPIFCM。该算法是使用NVIDIA的CUDA [13]框架和实验实现的，在包含64GB RAM，8个内核和带有3072 SP内核和12GB GPU内存的服务器上执行的实验。 我们的结果表明，该算法的并行版本的性能比原始顺序版本快27倍，并且比GAIFCM算法快68倍。我们表明，由于GPU中核心的利用率更好，平行版的加速会增加图像的大小。此外，与其他通用变体（例如IFCMPSO和GAIFCM）相比，我们在BrainWeb实验中显示了高达5倍的加速度。
In this paper we implemented the algorithm we developed in [1] called 3DPIFCM in a parallel environment by using CUDA on a GPU. In our previous work we introduced 3DPIFCM which performs segmentation of images in noisy conditions and uses particle swarm optimization for finding the optimal algorithm parameters to account for noise. This algorithm achieved state of the art segmentation accuracy when compared to FCM (Fuzzy C-Means), IFCMPSO (Improved Fuzzy C-Means with Particle Swarm Optimization), GAIFCM (Genetic Algorithm Improved Fuzzy C-Means) on noisy MRI images of an adult Brain. When using a genetic algorithm or PSO (Particle Swarm Optimization) on a single machine for optimization we witnessed long execution times for practical clinical usage. Therefore, in the current paper our goal was to speed up the execution of 3DPIFCM by taking out parts of the algorithm and executing them as kernels on a GPU. The algorithm was implemented using the CUDA [13] framework from NVIDIA and experiments where performed on a server containing 64GB RAM , 8 cores and a TITAN X GPU with 3072 SP cores and 12GB of GPU memory. Our results show that the parallel version of the algorithm performs up to 27x faster than the original sequential version and 68x faster than GAIFCM algorithm. We show that the speedup of the parallel version increases as we increase the size of the image due to better utilization of cores in the GPU. Also, we show a speedup of up to 5x in our Brainweb experiment compared to other generic variants such as IFCMPSO and GAIFCM.
