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Nov 13, 2018 06:11 PM | Greg Fleishman
Metrics, global or local
I looked around in the help outputs and documentation for the
answer to this question but couldn't find it. I took a look at the
source code as well, and it would be non-trivial to find the answer
amongst the hundreds of files, so I'm posting here. CMTK offers
metrics:
nmi
mi
cr
msd
ncc
When performing a registration, which of these metrics is computed globally over the whole image, and which are computed locally over neighborhoods? Some are obvious, e.g. msd doesn't normalize for any intensity statistics, so it's degenerately global (no local normalization) and local (uses single voxel neighborhoods). I suspect the mi metrics are global, because you want as many samples as possible to construct the histograms. So, in particular, I'm wondering about cr and ncc. When considering the "mean" and "standard deviations" in these functionals, are they computed from all voxels in the image, or locally for neighborhoods around each voxel.
Follow up question, is it different when computing the functional value vs. computing the residual (gradient of functional)?
Thanks very much in advance!
nmi
mi
cr
msd
ncc
When performing a registration, which of these metrics is computed globally over the whole image, and which are computed locally over neighborhoods? Some are obvious, e.g. msd doesn't normalize for any intensity statistics, so it's degenerately global (no local normalization) and local (uses single voxel neighborhoods). I suspect the mi metrics are global, because you want as many samples as possible to construct the histograms. So, in particular, I'm wondering about cr and ncc. When considering the "mean" and "standard deviations" in these functionals, are they computed from all voxels in the image, or locally for neighborhoods around each voxel.
Follow up question, is it different when computing the functional value vs. computing the residual (gradient of functional)?
Thanks very much in advance!
Threaded View
Title | Author | Date |
---|---|---|
Greg Fleishman | Nov 13, 2018 | |
Torsten Rohlfing | Nov 15, 2018 | |