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Contrast-to-Noise based Metric of Denoising Algorithms for Liver Vein Segmentation

机译:用于肝静脉分割的去噪算法的对比度噪声度量

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We analyse CT image denoising when applied to vessel segmentation. Proposed semi-global quality metric based on the contrast-to-noise ratio allowed us to estimate initial image quality and efficiency of denoising procedures without prior knowledge about a noise-free image. We show that the total variance filtering in L1 metric provides the best denoising when compared to other well-known denoising procedures such as non-local means denoising or anisotropic diffusion. Computational complexity of this denoising algorithm is addressed by comparing its implementation for Intel MIC and for NVIDIA CUDA HPC systems.
机译:当施加到血管分割时,我们分析CT图像去噪。 基于对比度与对比度的基于对比度的半全局质量指标,允许我们估计初始图像质量和去噪程序的效率而无需对无噪声图像的知识。 我们表明,与其他众所周知的去噪程序(例如非局部意味着去噪或各向异性扩散)相比,L1度量中的总方差过滤提供了最佳的去噪。 通过比较Intel MIC和NVIDIA CUDA HPC系统的实现来解决这种去噪算法的计算复杂性。

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