Afni3dpc
Afni3dpc is a wrapper for the AFNI (RW Cox, NIH) 3dpc application.
3dpc -- Program to compute principal components from multiple 3d datasets.
3dpc was written by Medical College of Wisconsin.
For more information about the AFNI package, see the AFNI Homepage
To see the AFNI postscript documentation please go here.
To read the help documentation generated from the command line go here.
To learn about afni data types follow this link.
Invocation
java Afni3dpc
This starts up the java interpreter and runs the Afni3dpc
application. You need to have
set up your environment for java
in order for this to work.
Afni3dpc window
Information to be entered
- Image dataset(s)
When selected, the datasets can be specified by one with the Browse and Brick buttons.
- Image directory
When chosen, all of the datasets in the given directory will be used.
- Image filelist
When chosen, all of the datasets listed in this text file will be used.
- Brick...
- Prefix
This is the prefix for the output dataset containing the eigen-bricks.
The default prefix is pc.
- Verbose
Enable progress reports will be displayed during computation.
- Save eigen-bricks as floats
Write the eigen-bricks as floats.
Usually, the eigen-bricks are written as shorts and scaled so that the
absolute value of the global maximum is equal to 10000.
- Display graph
Graph the first 3 eigen-timeseries. If there are less than
3 eigen-timeseries available, then all of them will be graphed.
- Dummy lines for each timeseries file
A number of additional lines will be added to the top of each eigen-timeseries.
The additional lines will have the value 999999. Any graphs displayed may be hard to read if
dummy lines are added.
- Number of components to save
Write the number of components specified to output.
Please note that the number of components can't be more that the number of input bricks.
- Remove mean from each input brick
Remove the mean from each input brick (across space).
- Normalize each input voxel timeseries
L2 normalize each input voxel time series.
Please note that this operation occurs after means are removed.
- Remove mean from each input voxel
Remove the mean from each input voxel (across bricks).
- Normalize each input brick
L2 normalize each input brick (after mean subtaction).
Please note that this operation occurs after means are removed.
- Mask dataset
This is the name of the dataset to use as a mask.
Last updated Mon Nov 18 14:30:31 EST 2002