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This old response (written shortly after our UK referendum on changing our voting system) may help here: ============================================================ 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 ============================================================ Personally, I wouldn't do any thresholding. Instead of counting GM or WM voxels, I would sum up the probabilities (and multiply by the volume of a voxel). Best regards, -John On 20 July 2011 07:19, Seyed Batouli <[log in to unmask]> wrote: > Dear All, > > I have segmented 750 T1 scans of 1.5T scanners, using the “New Segment”, > SPM8. This is a volumetric study. > > I am going to count the number of GM and WM voxels to calculate regional > brain volume. But it seems to me that I should perform a thresholding on the > GM and WM images, since visually I can say some non-zero voxels can never be > brain tissue. On the other hand, some voxels are regarded both as GM and WM. > > I have tried several thresholds, starting from 5% to 70%, but to me and just > visually 20% seemed the best. I just wanted please to ask you if you know > any certain level for this thresholding? My results of regional brain volume > seem dependant on this threshold level, and although the changes are not > very significant, since I have collected data from 3 different scanners it > becomes more important for me. > > Thanks for your response in advance, > > > > Regards, > > Amir