Convolution of an input image with a Gaussian kernel. Only Gaussian kernels with a diagonal covariance matrix are considered.
Input image:
Image to be filtered
sigmaX:
Standard deviation of Gaussian kernel in x-dimension
sigmaY:
Standard deviation of Gaussian kernel in y-dimension
sigmaZ:
Standard deviation of Gaussian kernel in z-dimension
sigmaT:
Standard deviation of Gaussian kernel in t-dimension
sigmaC:
Standard deviation of Gaussian kernel in c-dimension
sigma interpretation:
Standard deviations are interpreted to be given in pixels or in physical pixel size
Kernel truncation:
A factor to truncate the tails of the Gaussian function in terms of standard deviation, e.g. if kernel truncation is set to 2 then the Gaussian kernel is truncated (set to zero) for values farther than 2*(standard deviation) from the kernel's mean.
Boundary padding:
Padding of image: Method of how to simulate pixel values outside the image domain.
PADDING_ZERO: Values outside the image domain are assumed to be zero.
PADDING_BORDER: Values outside the image domain correspond to value of nearest pixel in the image domain.
PADDING_MIRROR: Values of the image are mirrored outside of the image domain along the image border.
PADDING_PERIODIC: Values are repeated, i.e. the image is assumed to be periodical with period equal to the image dimensions (as assumed for DFT)
Result image
The filtered image of type MTBImageType.MTB_DOUBLE