AFNI program: 3danisosmooth
Output of -help
Usage: 3danisosmooth [options] dataset
Smooths a dataset using an anisotropic smoothing technique.
The output dataset is preferentially smoothed to preserve edges.
Options :
-prefix pname = Use 'pname' for output dataset prefix name.
-iters nnn = compute nnn iterations (default=10)
-2D = smooth a slice at a time (default)
-3D = smooth through slices. Can not be combined with 2D option
-mask dset = use dset as mask to include/exclude voxels
-automask = automatically compute mask for dataset
Can not be combined with -mask
-viewer = show central axial slice image every iteration.
Starts aiv program internally.
-nosmooth = do not do intermediate smoothing of gradients
-sigma1 n.nnn = assign Gaussian smoothing sigma before
gradient computation for calculation of structure tensor.
Default = 0.5
-sigma2 n.nnn = assign Gaussian smoothing sigma after
gradient matrix computation for calculation of structure tensor.
Default = 1.0
-deltat n.nnn = assign pseudotime step. Default = 0.25
-savetempdata = save temporary datasets each iteration.
Dataset prefixes are Gradient, Eigens, phi, Dtensor.
Ematrix, Flux and Gmatrix are also stored for the first sub-brick.
Where appropriate, the filename is suffixed by .ITER where
ITER is the iteration number. Existing datasets will get overwritten.
-save_temp_with_diff_measures: Like -savetempdata, but with
a dataset named Diff_measures.ITER containing FA, MD, Cl, Cp,
and Cs values.
-phiding = use Ding method for computing phi (default)
-phiexp = use exponential method for computing phi
-noneg = set negative voxels to 0
-setneg NEGVAL = set negative voxels to NEGVAL
-edgefraction n.nnn = adjust the fraction of the anisotropic
component to be added to the original image. Can vary between
0 and 1. Default =0.5
-datum type = Coerce the output data to be stored as the given type
which may be byte, short or float. [default=float]
-matchorig - match datum type and clip min and max to match input data
-help = print this help screen
References:
Z Ding, JC Gore, AW Anderson, Reduction of Noise in Diffusion
Tensor Images Using Anisotropic Smoothing, Mag. Res. Med.,
53:485-490, 2005
J Weickert, H Scharr, A Scheme for Coherence-Enhancing
Diffusion Filtering with Optimized Rotation Invariance,
CVGPR Group Technical Report at the Department of Mathematics
and Computer Science,University of Mannheim,Germany,TR 4/2000.
J.Weickert,H.Scharr. A scheme for coherence-enhancing diffusion
filtering with optimized rotation invariance. J Visual
Communication and Image Representation, Special Issue On
Partial Differential Equations In Image Processing,Comp Vision
Computer Graphics, pages 103-118, 2002.
Gerig, G., Kubler, O., Kikinis, R., Jolesz, F., Nonlinear
anisotropic filtering of MRI data, IEEE Trans. Med. Imaging 11
(2), 221-232, 1992.
INPUT DATASET NAMES
-------------------
This program accepts datasets that are modified on input according to the
following schemes:
'r1+orig[3..5]' {sub-brick selector}
'r1+orig<100..200>' {sub-range selector}
'r1+orig[3..5]<100..200>' {both selectors}
'3dcalc( -a r1+orig -b r2+orig -expr 0.5*(a+b) )' {calculation}
For the gruesome details, see the output of 'afni -help'.
++ Compile date = Dec 17 2024 {AFNI_24.3.10:linux_ubuntu_24_64}
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