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An improved parallel fuzzy connected image segmentation method based on CUDA

机译:一种改进的基于CUDA的并行模糊连接图像分割方法

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摘要

PurposeFuzzy connectedness method (FC) is an effective method for extracting fuzzy objects from medical images. However, when FC is applied to large medical image datasets, its running time will be greatly expensive. Therefore, a parallel CUDA version of FC (CUDA-kFOE) was proposed by Ying et al. to accelerate the original FC. Unfortunately, CUDA-kFOE does not consider the edges between GPU blocks, which causes miscalculation of edge points. In this paper, an improved algorithm is proposed by adding a correction step on the edge points. The improved algorithm can greatly enhance the calculation accuracy.
机译:目的模糊连接方法(FC)是一种从医学图像中提取模糊对象的有效方法。但是,将FC应用于大型医学图像数据集时,其运行时间将非常昂贵。因此,Ying等人提出了并行的FC CUDA版本(CUDA-kFOE)。加速原FC。不幸的是,CUDA-kFOE没有考虑GPU块之间的边缘,这会导致边缘点的计算错误。本文提出了一种改进的算法,在边缘点上增加了校正步骤。改进后的算法可以大大提高计算精度。

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