-Apologies, resubmitting a previous post with the revised section between ====
thanks so much for your super fast responses! Your help is greatly
appreciated by an FSL/structural data processing newbie like me :)
To make sure I understand, would you mean to do the segmentation in native
space, and then use FLIRT and an estimated affine matrix to bring the tissue
images to MNI space, e.g. using the command:
flirt -in tissue_image -ref refvol -out tissue_image_in_MNI -init
On another note:
I am also interested in using priors with FAST to get a better tissue map
for basal ganglia, using the -a -P options. Would you have any comments on
the use of priors? (I trace structures on images transformed to MNI space,
and want the segmented tissue images in MNI space to feed them to a machine
learning program, along with the traced structures, to facilitate automatic
Briefly, these are the steps I am thinking of following - I would greatly
appreciate it if you could let me know if I do something stupid or if you
have any other comments:
1. Convert axial T1 ANALYZE to .nii.gz (fslchfiletype)
2. Use BET to remove skull etc.
(I have found that using a two stage procedure, with [i] -S -f 0.4 and then
[ii] -f 0.4 works pretty well in most cases in totally preserving the brain
removing all the rest. A further BET -f 0.4 will be applied if required).
3. Do FAST to get a bias-corrected brain image (the idea being
that registration may work better with bias corrected images)
e.g. : fast -b -B -o output_T1 input_T1_from
4. Do FLIRT :
[a] to get the bias-corrected T1 from  to MNI space AND
[b] to obtain an affine matrix.
e.g. : flirt -cost normmi -interp sinc -dof 9 -search x -180 180 -searchy
-180 180 -searchz -180 180 -in bias_cor_T1_from -ref MNI1mm -omat .xfm
5. Do FAST again on the native space, bias-corrected image from  to get
segmented tissue in native space, with the affine matrix from  to use
with priors (-a -P)
e.g. : fast -g -a .xfm -P -N (no bias cor.) -p -o output_T1 input_T1_from
6. Use FLIRT again with segmented tissue images from , and apply the
affine matrix from  to get the segmented tissue to MNI space too.
e.g. : flirt -in tissue_image -ref MNI1mm -out tissue_image_in_MNI -init
=================END OF REVISED SECTION=============
Thanks so much for your help!
On Wed, 12 Aug 2009 19:51:41 +0100, Mark Jenkinson <[log in to unmask]> wrote:
>I think you've answered your own question.
>It is step (e) which is the problematic one.
>We always recommend doing segmentation in the native image
>space, as transforming to another space involves interpolation
>which blurs the intensities, making the distinctions between
>tissues less clear and the histogram less well defined.
>So just avoid the resampling, do your segmentation in the
>native space and resample your resulting segmented images
>if you want them in a different space.
>All the best,