Like voting systems, if there are more than two alternatives, there is
no single best approach for picking the winner. To avoid such holes,
you may wish to consider a first past the post system, where you
assign membership based on what class has the largest probability.
However, this may not be so effective if you consider a voxel with a
35% probability of being GM, 25% probability of being WM and a 40%
probability of being scalp. This voxel would be labelled as scalp,
but is more likely to be brain than non-brain.
FSL segmentation incorporates an MRF model, which uses neighbourhood
information to inform class membership. This has the effect of
shifting the belonging probabilities closer to either 0 or 1, thus
reducing the ambiguity.
Best regards,
-John
On 1 June 2011 19:54, Greggory Rothmeier <[log in to unmask]> wrote:
> I'm using SPM8 and 'New Segmentation' to segment the single_subj_T1.nii file
> in the canonical folder. I then import them into matlab and use round() to
> set each element to a binary tissue (1) or no tissue (0). If I do this for
> each of the tissue types and then add them together, I end up with gaps in
> the data. I assume that this is because SPM didn't have enough confidence
> in it being a particular type of tissue or it's a mixture of different
> types. I've also done segmentation with FSL
> (http://www.fmrib.ox.ac.uk/fsl/) and it outputs segmented files that fit
> together like I'm looking for, but it doesn't have as many tissue types. Is
> there a setting within SPM8 to force it to make a decision about the tissue
> type so that the resulting segmented files fit together without gaps?
> Thank you,
> Greggory
>
> --
> Greggory Rothmeier
> Optoelectronics Laboratory
> Department of Physics and Astronomy
> Georgia State University
> 29 Peachtree Center Ave.
> Science Annex 400 Atlanta, GA 30303
> (404) 413-6042
> (770) 656-1671
>
|