Hi Joshua,
The "correct" way to obtain binarized maps would be to visit each voxel in turn, compute the probability that it belongs to each of the subfieds, and pick the subfield with the highest such probability.
Having said that, it should not be the case that there are two or more subfields with value > 150 in any voxel. I'm representing probabilities with numbers between 0 and 255 to save disk space, and while this may introduce some rounding off errors, the sum over all subfields should still sum to something around 255 (unless there is a big bug in my code)...
Koen
On Tue, Feb 14, 2012 at 6:59 PM, Joshua Lee jkilee@ucdavis.edu wrote:
Dear experts,
The hippocampal-subfield are expressed as probability maps. For each subfield, each voxel is assigned a probability of inclusion on a scale of [0-255]=[0-1]. However, If I were to select only voxels from each subfield's probability map corresponding to a >50% chance (i.e. mri_binarize -min 150), then I would expect that no subfield would overlap with another (i.e. a voxel shouldn't have greater than 50% probability of belonging CA1, AND a greater than 50% probability of belonging to CA4-DG. However it appears that this is not the case, and overlap does occur. Thus, unless I messed up, subfields are not mutually exclusive. Have I got this right? And if so, how do I obtain binary maps of subfields with no overlap?
Thanks
Josh
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