mri_normalize
mri_normalize <input vol> <output vol>
Normalize the white-matter, optionally based on control points. The input volume is converted into a new volume where white matter image values all range around 110.
Argument | Explanation |
---|---|
<input vol> | input mri volume file |
<output vol> | output mri volume file |
Argument | Explanation |
---|---|
-n <int n> | use n 3d normalization iterations (default=2) |
-no1d | disable 1d normalization |
-conform | interpolate and embed volume to be 256^3 |
-noconform | do not conform the volume |
-gentle | perform kinder gentler normalization |
-f <path to file> | use control points file (usually control.dat) |
-fonly <fname> | use only control points 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) |
-prune <boolean> | turn pruning of control points on/off (default=off). pruning useful if white is expanding into gm |
-MASK maskfile | |
-monkey | turns off 1d, sets num_3d_iter=1 |
-nosnr | disable snr normalization |
-sigma sigma | smooth bias field |
-aseg aseg | |
-v Gvx Gvy Gvz | for debugging |
-d Gx Gy Gz | for debugging |
-r controlpoints biasfield | for reading |
-surface <surface> <xform> | normalize based on the skelton of the interior of the transformed surface |
-u or -h | print usage |
mri_normalize SUBJECT/mri/nu.mgz SUBJECT/mri/T1.mgz
Uses the nu volume (nonuniformity corrected volume), and creates the T1 volume, with white matter voxels around 110 image value
mri_normalize -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
\"Cortical Surface-Based Analysis I: Segmentation and Surface Reconstruction\", Dale, A.M., Fischl, B., Sereno, M.I. (1999) NeuroImage 9(2):179-194
Report bugs to <freesurfer@nmr.mgh.harvard.edu>
nu_correct, mri_fill