I am doing VBM on two study groups. For each subject I have gray and white
matter segments, members of each group having been segmented from fully
normalized raw images (optimized VBM) using a template specific to their
respective groups (I am hoping to get the best possible segmentation this
way, by assuming that the groups have distinct gray/white matter
distributions). I would like to normalize these segments to an average
template I have created from all subjects in the study, so as not to create
a bias toward one group or the other in my analysis. I didn't expect
normalization to be a problem as I can invert the deformations performed on
each subject and normalize everyone to the average template. However, I have
read a posting from 9 Feb 2004 indicating that resampling using a logit
transform would be appropriate if I am going to warp probability images.
While I understand what a logit transform is, I am not clear on how to apply
this transformation to my data. In particular when I have experimented with
using Imcalc on one image, the voxels with probability = 1 cause division by
zero and an error. Has anyone used this method? Thanks for your help.
Ken Rando
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