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.
翻译:图像的获取和分割很可能会引入噪音。 进一步的图像处理,如图像登记和参数化等,可以引入更多的噪音。 因此,必须减少噪音测量和增强信号。 为了提高信号对噪音比和随后随机实地理论基于统计推理所需的数据的顺畅性,有必要采取某种平滑方法。 在许多图像平滑方法中,高山内核的平滑由于数字执行的简单化,已成为脑成像研究人员中一种事实上的平滑技术。 高山内核的平滑也增加了统计敏感性和统计能力以及高山性。 高山内核的平滑可以被视为对 voxel 值的加权平均值。 然后从中央界限的理论中,加权平均值应该更加高山化。