grids_pca

Perform principal component analysis on deformation grid files.

grids_pca [options] <training_list> <output_prefix>

DESCRIPTION

grids_pca performs Principal Component Analysis (PCA) on a set of deformation grid files listed in a training file. Each line of the training list specifies a deformation grid volume. The tool computes the mean deformation field and the principal components of variation, writing the results with the specified output prefix.

This is useful for building statistical models of anatomical variability from a set of non-linear registration results.

OPTIONS

--verbose

Print progress information during processing.

--clobber

Overwrite existing output files.

--mask mask.mnc

Restrict PCA computation to voxels within the specified binary mask.

--threshold f

Set the variance threshold for retaining principal components. Components are retained until the cumulative explained variance reaches this fraction. Default: 0.98.

--nomean

Do not subtract the mean deformation field before computing PCA.

--cache

Cache input grid files in memory for faster processing.

--normalize

Normalize the input deformation fields before PCA.

EXAMPLES

Compute PCA on a set of deformation grids:

grids_pca training_list.txt output_pca_

Compute PCA with a mask and 95% variance threshold:

grids_pca --mask brainmask.mnc --threshold 0.95 training_list.txt output_pca_

AUTHOR

Vladimir S. Fonov - McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University.

COPYRIGHTS

Copyright © 2011 by Vladimir S. Fonov

SEE ALSO

volumes_pca, volumes_pca_preprocess