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| || -nonmax_suppress (0/1) || turn non-maximum suppression on (1) or off (0) when using interior of surfaces || | |
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|| -fonly <fname> || use only control points file || || -lonly <fname> || use only control points in label file || || -label <fname> || use control points in label file || |
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| || -v ||verbose|| | |
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|| -MASK maskfile || || || -atlas <path to gca> <path to gca transform> <min distance of control points from non-brain> || use atlas to exclude control points from being in non-brain regions || || -noskull || || || -monkey || turns off 1d, sets num_3d_iter = 1 || || -nosnr || disable snr normalization || || -sigma sigma || smooth bias field || || -aseg aseg || || || -renorm volume || load volume and use all points in it that are exactly 110 as control points || || -checknorm volume min max || load volume and remove all control points that aren't in [min max] in volume || || -r controlpoints biasfield || for reading || || -surface <surface> <xform> || normalize based on the skelaton oft he interior or the transformed surface || || -v Gvx Gvy Gvz || for debugging || || -d Gx Gy Gz || for debugging || |
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Uses the norm volume, and creates the brain volume, making use of the aseg and masking with brainmask. |
Index
Contents
Name
mri_normalize - converts orig or nu volume into normalized white matter volume
Synopsis
mri_normalize <input directory> <output directory>
Arguments
Positional Arguments
<input directory> |
input directory |
<output directory> |
output directory |
Required Flagged Arguments
none
Optional Flagged Arguments
-no1d |
disable 1d normalization |
-nonmax_suppress (0/1) |
turn non-maximum suppression on (1) or off (0) when using interior of surfaces |
-conform |
interpolate and embed volume to be 256^3 |
-gentle |
perform kinder gentler normalization |
-f <path to file> |
use control points file (usually control.dat) |
-fonly <fname> |
use only control points file |
-lonly <fname> |
use only control points in label file |
-label <fname> |
use control points in label file |
-w <mri_vol c> <mri_vol b> |
write ctrl point(c) and bias field(b) volumes |
-a <float a> |
use control point with intensity a above target (default=25.0) |
-b <float b> |
use control point with intensity b below target (default=10.0) |
-g <float g> |
use max intensity/mm gradient g (default=1.000) |
-n <int n> |
use n 3d normalization iterations (default=2) |
-u |
print usage |
-prune <boolean> |
turn pruning of control points on/off (default=off). Useful if white is expanding into gm |
-MASK maskfile |
|
-atlas <path to gca> <path to gca transform> <min distance of control points from non-brain> |
use atlas to exclude control points from being in non-brain regions |
-noskull |
|
-monkey |
turns off 1d, sets num_3d_iter = 1 |
|| -nosnr || disable snr normalization ||
-sigma sigma |
smooth bias field |
-aseg aseg |
|
-renorm volume |
load volume and use all points in it that are exactly 110 as control points |
-checknorm volume min max |
load volume and remove all control points that aren't in [min max] in volume |
-r controlpoints biasfield |
for reading |
-surface <surface> <xform> |
normalize based on the skelaton oft he interior or the transformed surface |
-v Gvx Gvy Gvz |
for debugging |
-d Gx Gy Gz |
for debugging |
Outputs
wm |
wm volume of the cortical reconstruction is used as the input for mri_fill |
Description
mri_normalize converts orig or nu volume from cortical reconstruction as input into a new volume where white matter image values all range around 110
Examples
Example 1
mri_normalize SUBJECT/mri/nu SUBJECT/mri/wm
Uses the nu volume (nonuniformity corrected volume), and creates the wm volume, with white matter voxels around 110 image value
Example 2
mri_normalize -noskull -aseg aseg.mgz -mask brainmask.mgz norm.mgz brain.mgz
Uses the norm volume, and creates the brain volume, making use of the aseg and masking with brainmask.
Bugs
None
See Also
Links
Methods Description
description description
References
"Cortical Surface-Based Analysis I: Segmentation and Surface Reconstruction", Dale, A.M., Fischl, B., Sereno, M.I. (1999) NeuroImage 9(2):179-194
Reporting Bugs
Report bugs to <analysis-bugs@nmr.mgh.harvard.edu>