论文标题
高斯内核平滑
Gaussian kernel smoothing
论文作者
论文摘要
图像采集和细分可能会引入噪声。进一步的图像处理，例如图像注册和参数化可以引入其他噪声。因此，必须减少噪声测量并增强信号。为了增加信噪比（SNR）和基于随后的随机场理论所需的数据的平滑度，需要某种类型的平滑性。在许多图像平滑方法中，由于其在数值实现中的简单性，高斯内核平滑度已成为一种事实上的平滑技术。高斯内核平滑还增加了统计灵敏度和统计能力以及高斯性。高斯内核平滑可以看作是体素值的加权平均值。然后从中心极限定理，加权平均值应该更加高斯。
Image acquisition and segmentation are likely to introduce noise. Further image processing such as image registration and parameterization can introduce additional noise. It is thus imperative to reduce noise measurements and boost signal. In order to increase the signal-to-noise ratio (SNR) and smoothness of data required for the subsequent random field theory based statistical inference, some type of smoothing is necessary. Among many image smoothing methods, Gaussian kernel smoothing has emerged as a de facto smoothing technique among brain imaging researchers due to its simplicity in numerical implementation. Gaussian kernel smoothing also increases statistical sensitivity and statistical power as well as Gausianness. Gaussian kernel smoothing can be viewed as weighted averaging of voxel values. Then from the central limit theorem, the weighted average should be more Gaussian.
