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,
On 12 Aug 2009, at 19:28, Yannis Paloyelis wrote:
> Dear FSL users,
> Problems galore!
> I get FAST segmentation problems. When I specify:
> fast -g -b -B -o output_image -p input_image (input_image=a
> standardised to
> MNI, brain only, T1.nii.gz), I get the message:
> Exception: Not enough classes detected to init KMeans.
> Following from this, when I specify:
> [P1] fast -g -a (matrix from FLIRT) -b -B -o output_image -v -p
> (using prior to initialise parameter estimation) OR
> [P2] fast -g -a (matrix from FLIRT) -b -B -o output_image -P -v -p
> input_image (using priors throughout)
> I get the problematic images I have attached. I have checked the input
> images and they are fine (previous steps: (a)ANALYZE T1(original) ->
> (b)NIFTI-> (c)fslswapsim-> (d) bet-> (e)FLIRT). I get the same
> message even when, regarding the input image, I omit steps (c), or
> steps (b) AND (c).
> HOWEVER, command  works fine (and [P1] has done so previously)
> when the
> input image has not been through FLIRT (i.e. step (e) was omitted,
> and the
> input image to FAST is not registered to MNI).
> Any ideas of what I may be doing wrong?
> Thanks so much for your help!