Description Usage Arguments Value Author(s) References See Also Examples
Simplified, low-parameter eigenanatomy implemented with deflation. The
algorithm is able to automatically select hidden sparseness
parameters, given the key parameter nvecs
. The user should select the
cthresh
and smoother
regularization parameters for a given
application and also based on observing algorithm behavior when
verbose=TRUE
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
inmat |
input matrix |
nvecs |
number of eigenanatomy vectors to compute. see
|
mask |
input mask, must match matrix |
smoother |
regularization parameter, typically 0 or 0.5, in voxels |
cthresh |
remove isolated voxel islands of size below this value |
its |
number of iterations |
eps |
gradient descent parameter |
positivity |
return unsigned eigenanatomy vectors |
priors |
external initialization matrix. |
priorWeight |
weight on priors in range 0 to 1. |
sparEpsilon |
threshold that controls initial sparseness estimate |
whiten |
use ICA style whitening. |
verbose |
controls whether computation is silent or not. |
matrix is output, analogous to svd(mat,nu=0,nv=nvecs)
Avants BB, Tustison NJ
Kandel, B. M.; Wang, D. J. J.; Gee, J. C. & Avants, B. B. Eigenanatomy: sparse dimensionality reduction for multi-modal medical image analysis. Methods, 2015, 73, 43-53. PS Dhillon, DA Wolk, SR Das, LH Ungar, JC Gee, BB Avants Subject-specific functional parcellation via Prior Based Eigenanatomy NeuroImage, 2014, 99, 14-27.
eanatSelect
https://github.com/stnava/blindSourceSeparationInANTsR
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ## Not run:
mat <- matrix(rnorm(2000),ncol=50)
nv <- eanatSelect( mat, selectorScale = 1.2 )
esol <- eanatDef( mat, nvecs=nv )
es2 <- sparseDecom( mat, nvecs = nv )
print( paste( "selected", nrow(esol),'pseudo-eigenvectors') )
print( mean( abs( cor( mat %*% t(esol)) ) ) ) # what we use to select nvecs
networkPriors = getANTsRData("fmrinetworks")
ilist = networkPriors$images
mni = antsImageRead( getANTsRData("mni") )
mnireg = antsRegistration( meanbold*mask, mni, typeofTransform = 'Affine')
for ( i in 1:length(ilist) )
ilist[[i]] = antsApplyTransforms( meanbold,ilist[[i]],mnireg$fwdtransform )
pr = imageListToMatrix( ilist, cortMask )
esol <- eanatDef( boldMat,
nvecs = length(ilist), cortMask, verbose=FALSE,
cthresh = 25, smoother = 0, positivity = TRUE, its=10, priors=pr,
priorWeight=0.15, eps=0.1 )
## End(Not run)
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