The norm is probably the better volume because it has the most effective intensity normalization. --canorm performs the normalization by finding voxels that it thinks are extremely likely to get white matter. Assuming that all WM has the same intensity, it fits a smooth curve between the WM points using soap bubble smoothing. If then divides each voxel in the image by the value of the smooth curve at that voxel.Hi FS Expert
I have just looked for this subject a long the list but I haven't found anything about.
I have been working with FreeSurfer and I could get some segmentation from my T1 data. I used 'mri_segstats' to quantify volume of some subcortical region. Also I want to get intensity statistics from these regions, so I used --in parameter. In the 'recon-all' pipeline, you use 'norm.mgz' but..
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I wonder is not better to use nu.mgz in order to get intensity values more related to original data?
If is not. Can you explain me what '-canorm' step does? Because I don't get it
I'll be grateful if you can help
Being all for now, I await a reply
Guido PASCARIELLO
Facultad de Ingeniería - Universidad Nacional de Entre Ríos
Argentina
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