I took a look at the tiff file that you sent with the last email,
and the problem does look like it is due to the affine
registration. The segmented image is reduced in size because
the prior probability images do not accurately overlay onto the
image to segment.
One way of making this part more robust would be to disable
the brain masking, which is found under the spatial normalisation
part of the defaults.
The remainder of the email answers the previous questions ...
| I tried the spatial normalization and it works well.
| Although I do not know, how I can disable the
| nonlinear wraping ?
This can be done via the defaults button (spatial
normalisation, estimation), where you would specify
the number of DCT basis functions as [0 0 0].
| The segmentation is not generaly bad, only segmented
| areas in upper regions are incorect. I do not think,
| that any function is not working, because there are no
| error messages during segmentation. Exists any
| limitation for segmenting ?
The images segment best when you can get a good
registration between them and the prior probability
images. If the head is a slightly different shape, then
the affine registration may not model the overall head shape
that well. This is particularly important if the contrast
is not so high, in which case, the segmentation relies more
on the prior probability images.
| I use SPM'99 to segment 3-d gradient-echo datasets.(TR
| = 19ms, TE = 4,4 ms, flip = 8, FOV = 23 cm, thickness
| 3mm, gap = 0, slices = 60). I get good results, when I
| use the PD-weigthed template. Usually the segmentation
| is almost perfect. Sometimes (on some heads) the
| segmented areas are much too small, especialy in upper
| regions.
On some images, the intensity is lower further away from the
centre of the magnetic field. This can be problematic to
the segmentation, which is why SPM99 has an optional intensity
nonuniformity correction built into the segmentation. Although
this usually works well, it sometimes has problems because the
image contrast can be lower further away from the centre of
the magnet.
| I tried quite a lot to find the reason
| for that but I can't find a systematic. I have a
| couple of ideas: Does the ratio between FOV and head size
| have any influence on the segmentation ?
This can have a small effect on the segmentation. When assigning
probabilities for the voxels belonging in the different clusters,
the segmentation uses Bayesian rules. Part of these rules base
the belonging probabilities on the number of voxels that the cluster
is estimated to contain.
| How does the
| position of the head (i.e. rigth-left) influence
| the quality of the segmentation ?
This should not have any specific effect.
Best wishes,
-John
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