GPU accelerated fuzzy connected image segmentation by using CUDA.
GPU accelerated fuzzy connected image segmentation by using CUDA.
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DOI：
10.1109/iembs.2009.5333158
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Miller RW
中科院分区：
文献类型：
--
作者：
Zhuge Y;Cao Y;Miller RW
使用模糊连接性原理的图像分割技术在近年来在几个大型应用中分割了各种物体，这些算法的一个问题是处理大型图像时，它们的计算需求是较高的。 Unified Device Architecture (CUDA) platform for segmenting large medical image data sets. Our experiments based on three data sets with small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 7.2x, 7.3x, and 14.4x, correspondingly, for the three data sets over the sequential implementation of fuzzy connected image segmentation algorithm on CPU.
Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem of these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays commodity graphics hardware provides high parallel computing power. In this paper, we present a parallel fuzzy connected image segmentation algorithm on Nvidia’s Compute Unified Device Architecture (CUDA) platform for segmenting large medical image data sets. Our experiments based on three data sets with small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 7.2x, 7.3x, and 14.4x, correspondingly, for the three data sets over the sequential implementation of fuzzy connected image segmentation algorithm on CPU.