Purpose Fuzzy 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. Methods In the improved method, an iterative manner is applied. In the first iteration, the affinity computation strategy is changed and a look up table is employed for memory reduction. In the second iteration, the error voxels because of asynchronism are updated again. Results Three different CT sequences of hepatic vascular with different sizes were used in the experiments with three different seeds. NVIDIA Tesla C2075 is used to evaluate our improved method over these three data sets. Experimental results show that the improved algorithm can achieve a faster segmentation compared to the CPU version and higher accuracy than CUDA-kFOE. Conclusions The calculation results were consistent with the CPU version, which demonstrates that it corrects the edge point calculation error of the original CUDA-kFOE. The proposed method has a comparable time cost and has less errors compared to the original CUDA-kFOE as demonstrated in the experimental results. In the future, we will focus on automatic acquisition method and automatic processing.
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机译：目的模糊连通性方法（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。方法在改进的方法中，采用了迭代方式。在第一次迭代中，更改了相似性计算策略，并使用了一个查询表来减少内存。在第二次迭代中，由于异步而导致的错误体素再次被更新。结果在三种不同种子的实验中，使用了三种不同大小的肝血管CT序列。 NVIDIA Tesla C2075用于评估这三个数据集的改进方法。实验结果表明，与CPU版本相比，改进后的算法可以实现更快的分割，并且比CUDA-kFOE精度更高。结论计算结果与CPU版本一致，这表明它可以纠正原始CUDA-kFOE的边缘点计算误差。实验结果表明，与原始CUDA-kFOE相比，该方法具有可比的时间成本，并且误差较小。将来，我们将专注于自动获取方法和自动处理。
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