AFNI program: 3dAutomask
Output of -help
Usage: 3dAutomask [options] dataset
Input dataset is EPI 3D+time, or a skull-stripped anatomical.
Output dataset is a brain-only mask dataset.
This program by itself does NOT do 'skull-stripping'. Use
program 3dSkullStrip for that purpose!
Method:
+ Uses 3dClipLevel algorithm to find clipping level.
+ Keeps only the largest connected component of the
supra-threshold voxels, after an erosion/dilation step.
+ Writes result as a 'fim' type of functional dataset,
which will be 1 inside the mask and 0 outside the mask.
Options:
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-prefix ppp = Write mask into dataset with prefix 'ppp'.
[Default == 'automask']
-apply_prefix ppp = Apply mask to input dataset and save
masked dataset. If an apply_prefix is given
and not the usual prefix, the only output
will be the applied dataset
-clfrac cc = Set the 'clip level fraction' to 'cc', which
must be a number between 0.1 and 0.9.
A small 'cc' means to make the initial threshold
for clipping (a la 3dClipLevel) smaller, which
will tend to make the mask larger. [default=0.5]
-nograd = The program uses a 'gradual' clip level by default.
To use a fixed clip level, use '-nograd'.
[Change to gradual clip level made 24 Oct 2006.]
-peels pp = Peel (erode) the mask 'pp' times,
then unpeel (dilate). Using NN2 neighborhoods,
clips off protuberances less than 2*pp voxels
thick. Turn off by setting to 0. [Default == 1]
-NN1 -NN2 -NN3 = Erode and dilate using different neighbor definitions
NN1=faces, NN2=edges, NN3= corners [Default=NN2]
Applies to erode and dilate options, if present.
Note the default peeling processes still use NN2
unless the peels are set to 0
-nbhrs nn = Define the number of neighbors needed for a voxel
NOT to be eroded. The 18 nearest neighbors in
the 3D lattice are used, so 'nn' should be between
6 and 26. [Default == 17]
-q = Don't write progress messages (i.e., be quiet).
-eclip = After creating the mask, remove exterior
voxels below the clip threshold.
-dilate nd = Dilate the mask outwards 'nd' times.
-erode ne = Erode the mask inwards 'ne' times.
-SI hh = After creating the mask, find the most superior
voxel, then zero out everything more than 'hh'
millimeters inferior to that. hh=130 seems to
be decent (i.e., for Homo Sapiens brains).
-depth DEP = Produce a dataset (DEP) that shows how many peel
operations it takes to get to a voxel in the mask.
The higher the number, the deeper a voxel is located
in the mask. Note this uses the NN1,2,3 neighborhoods
above with a default of 2 for edge-sharing neighbors
None of -peels, -dilate, or -erode affect this option.
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How to make an edge-of-brain mask from an anatomical volume:
* 3dSkullStrip to create a brain-only dataset; say, Astrip+orig
* 3dAutomask -prefix Amask Astrip+orig
* Create a mask of edge-only voxels via
3dcalc -a Amask+orig -b a+i -c a-i -d a+j -e a-j -f a+k -g a-k \
-expr 'ispositive(a)*amongst(0,b,c,d,e,f,g)' -prefix Aedge
which will be 1 at all voxels in the brain mask that have a
nearest neighbor that is NOT in the brain mask.
* cf. '3dcalc -help' DIFFERENTIAL SUBSCRIPTS for information
on the 'a+i' et cetera inputs used above.
* In regions where the brain mask is 'stair-stepping', then the
voxels buried inside the corner of the steps probably won't
show up in this edge mask:
...00000000...
...aaa00000...
...bbbaa000...
...bbbbbaa0...
Only the 'a' voxels are in this edge mask, and the 'b' voxels
down in the corners won't show up, because they only touch a
0 voxel on a corner, not face-on. Depending on your use for
the edge mask, this effect may or may not be a problem.
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++ Compile date = Dec 17 2024 {AFNI_24.3.10:linux_ubuntu_24_64}
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