NAME

mri_ca_label

SYNOPSIS

mri_ca_label [<options>] invol1 [invol2 ...] xform gcafile outvol

DESCRIPTION

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POSITIONAL ARGUMENTS

ArgumentExplanation
invol1 [invol2 ...]input volume(s)
xformtransform file
gcafile 
outvoloutput volume

REQUIRED-FLAGGED ARGUMENTS

None

OPTIONAL-FLAGGED ARGUMENTS

ArgumentExplanation
-cross-sequencelabel a volume acquired with sequence different than atlas
-nogibbsdisable gibbs priors
-wm <path>use wm segmentation
-conforminterpolate volume to be isotropic 1mm^3
-normpdnormalize PD image to GCA means
-tl <gca_tl>use file to label thin temporal lobe
-debug_voxel <x> <y> <z>debug voxel
-debug_node <x> <y> <z>debug node
-debug_label <int n>debug label
-tr <float n>set TR in msec
-te <float n>set TE in msec
-alpha <float n>set alpha in radians
-example <mri_vol> <segmentation>use T1 (mri_vol) and segmentation as example
-pthresh <float n>use p threshold n for adaptive renormalization (default=.7)
-niter <int n>apply max likelihood for n iterations (default=2)
-novardo not use variance in classification
-regularize <float n>regularize variance to be sigma+nC(noise)
-nohippodo not auto-edit hippocampus
-fwm <mri_vol>use fixed white matter segmentation from wm
-mri <mri_vol>write most likely MR volume to mri_vol
-heq <mri_vol>use histogram equalization from mri_vol
-renorm <mri_vol>renormalize using predicted intensity values in mri_vol
-flashuse FLASH forward model to predict intensity values
-flash_params <filename>use FLASH forward model and tissue params in filename to predict
-renormalize <wsize> <iter>renorm class means <iter> times after initial label with window of <wsize>
-r <mri_vol>set input volume
-huse GCA to histogram normalize input image
-a <int n>mean filter n time to conditional densities
-w <int n> <filename>write snapshots of gibbs process every n times to filename
-m <mri_vol>use mri_vol to mask final labeling
-e <int n>expand
-n <int n>set max iterations to n (default=200)
-f <int f> <float t>filter labeled volume with threshold t (default=.5) mode filter f (default=0)times
-L <mri_vol> <LTA>longitudinal processing: mri_vol is label from tp1, LTA is registration from tp1 to current data
-RELABEL_UNLIKELY <1/0> <wsize> <sigma> <thresh>reclassify voxels at least <thresh> std devs from the mean using a <wsize> Gaussian window (with <sigma> standard dev) to recompute priors and likelihoods

OUTPUTS

OutputExplanation
outvoloutput volume

EXAMPLE 1

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BUGS

None

REPORTING

Report bugs to <freesurfer@nmr.mgh.harvard.edu>

SEE-ALSO

mri_cc