Approximate separable 3D anisotropic Gauss filter

Anisotropie Gaussian filters are useful for adaptive smoothing and feature extraction. In our application, micro-tomographic images of fibers were smoothed by anisotropic Gaussians. In this case, this is more natural than using their isotropic counterparts. But filtering in large 3D data is very time consuming. We extend the work of Geusebroek et al. on fast Gauss filtering to three dimensions [(J-M Geusebroek et al., 2003), (G.Z. Yang et al., 1996)]. We propose an approximate separable filtering scheme which consists of three ID convolutions. Initial experiments suggest that this filter can outperform an FFT based implementation when the kernel size is small compared to the size of the 3D images.