Hi Marcos, 1. your fix of MRIread.m is great. I forgot of this bug; we should totally take care of it. 2. a problem is that nu.mgz (or norm.mgz) and the posteriors are in difference voxel space. So, you need to resample norm.mgz to the space of the subfields. To do so, you can use mri_convert with the option -rl ("reslice like"): mri_convert norm.mgz norm_resampled.mgz -rl posterior_subiculum.mgz 3. Now you can do: A=MRIread('norm.mgz'); data=A.vol(:); B=MRIread('posterior_subiculum.mgz'); post=double(B.vol(:)); mean=sum(data.*post)/sum(post); variance=sum((data-mean).^2.*post)/sum(post); Cheers, /Eugenio
On Mon, 2013-06-10 at 17:17 -0300, Marcos Martins da Silva wrote:
Hi, Eugenio TY for your fast help. I understood you were suggesting to compute that on MatLab and I tried this:
NU=MRIread('nu.mgz') ### that runs ok Ps=MRIread('posterior_left_subiculum.mgz') ### it fails with the following message WARNING: error reading MR params Attempted to access mr_parms(1); index out of bounds because numel(mr_parms)=0.
Error in MRIread (line 100) tr = mr_parms(1);
I solved creating a little customized nMRIread.m with the following changes:
if numel(mr_parms) > 0 tr = mr_parms(1); flip_angle = mr_parms(2); te = mr_parms(3); ti = mr_parms(4); else mr_parms(1) = 0; mr_parms(2) = 0; mr_parms(3) = 0; mr_parms(4) = 0; tr = mr_parms(1); flip_angle = mr_parms(2); te = mr_parms(3); ti = mr_parms(4); end
With those changes I assigned 0 to mr-parms elements so it runs without errors But whe I tried the following line I copy and pasted from your message: mean=sum(Ps.*NU)/sum(Ps); ### I got the following error Undefined function 'times' for input arguments of type 'struct'.
Any help? Cheers, Marcos
PS: I promised I will post the final solution to list. But I guess it is more didactic if these little problems are solved before. TY, again. Em Seg, 2013-06-10 às 14:19 -0400, Juan Eugenio Iglesias escreveu:
Hi Marcos, the right way of doing this is using the soft posteriors to compute the mean and variance, rather than thresholding at p=0.5. For instance, if you wanted to compute the mean and variance of the intensitites of the subiculum, you would do something like this: mean=sum(Ps.*NU)/sum(Ps); variance=sum(Ps.*(NU-mean).^2)/sum(Ps); (where Ps is the posterior of the subiculum) Cheers, /Eugenio
On Mon, 2013-06-10 at 15:12 -0300, Marcos Martins da Silva wrote:
Hi, Freesurfer Experts
After usual processing with recon-all -all we get the aseg.stats file with several data including intensity values like: normMean normStdDev normMin normMax normRange Left-Hippocampus 77.8939 7.5748 46.0000 105.0000 59.0000
How could I get similar results for each hippocampal subfield, assuming I also generated all posterior*.mgz files corresponding to each subfield? I guess I should first binarize each of the subfields file with a threshold=127 to map all the pertinent voxels, and then use these files as masks over nu.mgz and calculate the intensities values. But I do not know the best way to accomplish this after the binarize step.
Thank you in advance for any help.
Marcos _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Hi, Eugenio, TY again for your help. I did the computing without errors after your suggestions. I also computed the intensities based on nu.mgz, norm.mgz and T1.mgz. I would like to know the best measure I should consider, including because I intend to compare these values with intensity data from basal ganglia and cortical areas as I can find in aseg.stats. It follows my results: norm.mgz based meannorm= 78.725 variancenorm = 20.258 (stdev = 4.500888801)
nu.mgz based meannu = 87.362 variancenu = 26.343 (stdev =5.132543229)
T1.mgz based meant1 = 84.023 variancet1 = 23.277 (stdev =4.824624338)
It follows the MatLab/Octave code I used Norm=MRIread('norm_postres.mgz'); datanorm=Norm.vol(:); Ps=nMRIread('posterior_right_subiculum.mgz'); postsub=double(Ps.vol(:)); meannorm=sum(datanorm.*postsub)/sum(postsub); variancenorm=sum((datanorm-meannorm).^2.*postsub)/sum(postsub); Nu=MRIread('nu_postres.mgz'); datanu=Nu.vol(:); meannu=sum(datanu.*postsub)/sum(postsub); variancenu=sum((datanu-meannu).^2.*postsub)/sum(postsub); T1=MRIread('T1_postres.mgz'); datat1=T1.vol(:); meant1=sum(datat1.*postsub)/sum(postsub); variancet1=sum((datat1-meant1).^2.*postsub)/sum(postsub);
The *postres.mgz files are nu, norm and T1 resampled as you suggested. And nMRIread is the workarounded version of MRIread I said in previous message. Cheers, Marcos Em Seg, 2013-06-10 às 16:58 -0400, Juan Eugenio Iglesias escreveu:
Hi Marcos,
- your fix of MRIread.m is great. I forgot of this bug; we should
totally take care of it. 2. a problem is that nu.mgz (or norm.mgz) and the posteriors are in difference voxel space. So, you need to resample norm.mgz to the space of the subfields. To do so, you can use mri_convert with the option -rl ("reslice like"): mri_convert norm.mgz norm_resampled.mgz -rl posterior_subiculum.mgz 3. Now you can do: A=MRIread('norm.mgz'); data=A.vol(:); B=MRIread('posterior_subiculum.mgz'); post=double(B.vol(:)); mean=sum(data.*post)/sum(post); variance=sum((data-mean).^2.*post)/sum(post); Cheers, /Eugenio
On Mon, 2013-06-10 at 17:17 -0300, Marcos Martins da Silva wrote:
Hi, Eugenio TY for your fast help. I understood you were suggesting to compute that on MatLab and I tried this:
NU=MRIread('nu.mgz') ### that runs ok Ps=MRIread('posterior_left_subiculum.mgz') ### it fails with the following message WARNING: error reading MR params Attempted to access mr_parms(1); index out of bounds because numel(mr_parms)=0.
Error in MRIread (line 100) tr = mr_parms(1);
I solved creating a little customized nMRIread.m with the following changes:
if numel(mr_parms) > 0 tr = mr_parms(1); flip_angle = mr_parms(2); te = mr_parms(3); ti = mr_parms(4); else mr_parms(1) = 0; mr_parms(2) = 0; mr_parms(3) = 0; mr_parms(4) = 0; tr = mr_parms(1); flip_angle = mr_parms(2); te = mr_parms(3); ti = mr_parms(4); end
With those changes I assigned 0 to mr-parms elements so it runs without errors But whe I tried the following line I copy and pasted from your message: mean=sum(Ps.*NU)/sum(Ps); ### I got the following error Undefined function 'times' for input arguments of type 'struct'.
Any help? Cheers, Marcos
PS: I promised I will post the final solution to list. But I guess it is more didactic if these little problems are solved before. TY, again. Em Seg, 2013-06-10 às 14:19 -0400, Juan Eugenio Iglesias escreveu:
Hi Marcos, the right way of doing this is using the soft posteriors to compute the mean and variance, rather than thresholding at p=0.5. For instance, if you wanted to compute the mean and variance of the intensitites of the subiculum, you would do something like this: mean=sum(Ps.*NU)/sum(Ps); variance=sum(Ps.*(NU-mean).^2)/sum(Ps); (where Ps is the posterior of the subiculum) Cheers, /Eugenio
On Mon, 2013-06-10 at 15:12 -0300, Marcos Martins da Silva wrote:
Hi, Freesurfer Experts
After usual processing with recon-all -all we get the aseg.stats file with several data including intensity values like: normMean normStdDev normMin normMax normRange Left-Hippocampus 77.8939 7.5748 46.0000 105.0000 59.0000
How could I get similar results for each hippocampal subfield, assuming I also generated all posterior*.mgz files corresponding to each subfield? I guess I should first binarize each of the subfields file with a threshold=127 to map all the pertinent voxels, and then use these files as masks over nu.mgz and calculate the intensities values. But I do not know the best way to accomplish this after the binarize step.
Thank you in advance for any help.
Marcos _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Dear Marcos, these are just mean and standard deviations of intensities of the images at different levels of processing. What the best measure is depends on what you want to do with them. Cheers, /Eugenio
On Tue, 2013-06-11 at 11:46 -0300, Marcos Martins da Silva wrote:
Hi, Eugenio, TY again for your help. I did the computing without errors after your suggestions. I also computed the intensities based on nu.mgz, norm.mgz and T1.mgz. I would like to know the best measure I should consider, including because I intend to compare these values with intensity data from basal ganglia and cortical areas as I can find in aseg.stats. It follows my results: norm.mgz based meannorm= 78.725 variancenorm = 20.258 (stdev = 4.500888801)
nu.mgz based meannu = 87.362 variancenu = 26.343 (stdev =5.132543229)
T1.mgz based meant1 = 84.023 variancet1 = 23.277 (stdev =4.824624338)
It follows the MatLab/Octave code I used Norm=MRIread('norm_postres.mgz'); datanorm=Norm.vol(:); Ps=nMRIread('posterior_right_subiculum.mgz'); postsub=double(Ps.vol(:)); meannorm=sum(datanorm.*postsub)/sum(postsub); variancenorm=sum((datanorm-meannorm).^2.*postsub)/sum(postsub); Nu=MRIread('nu_postres.mgz'); datanu=Nu.vol(:); meannu=sum(datanu.*postsub)/sum(postsub); variancenu=sum((datanu-meannu).^2.*postsub)/sum(postsub); T1=MRIread('T1_postres.mgz'); datat1=T1.vol(:); meant1=sum(datat1.*postsub)/sum(postsub); variancet1=sum((datat1-meant1).^2.*postsub)/sum(postsub);
The *postres.mgz files are nu, norm and T1 resampled as you suggested. And nMRIread is the workarounded version of MRIread I said in previous message. Cheers, Marcos Em Seg, 2013-06-10 às 16:58 -0400, Juan Eugenio Iglesias escreveu:
Hi Marcos,
- your fix of MRIread.m is great. I forgot of this bug; we should
totally take care of it. 2. a problem is that nu.mgz (or norm.mgz) and the posteriors are in difference voxel space. So, you need to resample norm.mgz to the space of the subfields. To do so, you can use mri_convert with the option -rl ("reslice like"): mri_convert norm.mgz norm_resampled.mgz -rl posterior_subiculum.mgz 3. Now you can do: A=MRIread('norm.mgz'); data=A.vol(:); B=MRIread('posterior_subiculum.mgz'); post=double(B.vol(:)); mean=sum(data.*post)/sum(post); variance=sum((data-mean).^2.*post)/sum(post); Cheers, /Eugenio
On Mon, 2013-06-10 at 17:17 -0300, Marcos Martins da Silva wrote:
Hi, Eugenio TY for your fast help. I understood you were suggesting to compute that on MatLab and I tried this:
NU=MRIread('nu.mgz') ### that runs ok Ps=MRIread('posterior_left_subiculum.mgz') ### it fails with the following message WARNING: error reading MR params Attempted to access mr_parms(1); index out of bounds because numel(mr_parms)=0.
Error in MRIread (line 100) tr = mr_parms(1);
I solved creating a little customized nMRIread.m with the following changes:
if numel(mr_parms) > 0 tr = mr_parms(1); flip_angle = mr_parms(2); te = mr_parms(3); ti = mr_parms(4); else mr_parms(1) = 0; mr_parms(2) = 0; mr_parms(3) = 0; mr_parms(4) = 0; tr = mr_parms(1); flip_angle = mr_parms(2); te = mr_parms(3); ti = mr_parms(4); end
With those changes I assigned 0 to mr-parms elements so it runs without errors But whe I tried the following line I copy and pasted from your message: mean=sum(Ps.*NU)/sum(Ps); ### I got the following error Undefined function 'times' for input arguments of type 'struct'.
Any help? Cheers, Marcos
PS: I promised I will post the final solution to list. But I guess it is more didactic if these little problems are solved before. TY, again. Em Seg, 2013-06-10 às 14:19 -0400, Juan Eugenio Iglesias escreveu:
Hi Marcos, the right way of doing this is using the soft posteriors to compute the mean and variance, rather than thresholding at p=0.5. For instance, if you wanted to compute the mean and variance of the intensitites of the subiculum, you would do something like this: mean=sum(Ps.*NU)/sum(Ps); variance=sum(Ps.*(NU-mean).^2)/sum(Ps); (where Ps is the posterior of the subiculum) Cheers, /Eugenio
On Mon, 2013-06-10 at 15:12 -0300, Marcos Martins da Silva wrote:
Hi, Freesurfer Experts
After usual processing with recon-all -all we get the aseg.stats file with several data including intensity values like: normMean normStdDev normMin normMax normRange Left-Hippocampus 77.8939 7.5748 46.0000 105.0000 59.0000
How could I get similar results for each hippocampal subfield, assuming I also generated all posterior*.mgz files corresponding to each subfield? I guess I should first binarize each of the subfields file with a threshold=127 to map all the pertinent voxels, and then use these files as masks over nu.mgz and calculate the intensities values. But I do not know the best way to accomplish this after the binarize step.
Thank you in advance for any help.
Marcos _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Hi, Eugenio Hmmm, I guessed the values I got were different because they represented different stages of the processing. And you just confirmed that. Thank you. I would like to compare these intensities with the intensities I can read in aseg.stats. And I guess the comparison would be more interesting if I use the same source. Cheers, Marcos Em Ter, 2013-06-11 às 17:13 -0400, Juan Eugenio Iglesias escreveu:
Dear Marcos, these are just mean and standard deviations of intensities of the images at different levels of processing. What the best measure is depends on what you want to do with them. Cheers, /Eugenio
On Tue, 2013-06-11 at 11:46 -0300, Marcos Martins da Silva wrote:
Hi, Eugenio, TY again for your help. I did the computing without errors after your suggestions. I also computed the intensities based on nu.mgz, norm.mgz and T1.mgz. I would like to know the best measure I should consider, including because I intend to compare these values with intensity data from basal ganglia and cortical areas as I can find in aseg.stats. It follows my results: norm.mgz based meannorm= 78.725 variancenorm = 20.258 (stdev = 4.500888801)
nu.mgz based meannu = 87.362 variancenu = 26.343 (stdev =5.132543229)
T1.mgz based meant1 = 84.023 variancet1 = 23.277 (stdev =4.824624338)
It follows the MatLab/Octave code I used Norm=MRIread('norm_postres.mgz'); datanorm=Norm.vol(:); Ps=nMRIread('posterior_right_subiculum.mgz'); postsub=double(Ps.vol(:)); meannorm=sum(datanorm.*postsub)/sum(postsub); variancenorm=sum((datanorm-meannorm).^2.*postsub)/sum(postsub); Nu=MRIread('nu_postres.mgz'); datanu=Nu.vol(:); meannu=sum(datanu.*postsub)/sum(postsub); variancenu=sum((datanu-meannu).^2.*postsub)/sum(postsub); T1=MRIread('T1_postres.mgz'); datat1=T1.vol(:); meant1=sum(datat1.*postsub)/sum(postsub); variancet1=sum((datat1-meant1).^2.*postsub)/sum(postsub);
The *postres.mgz files are nu, norm and T1 resampled as you suggested. And nMRIread is the workarounded version of MRIread I said in previous message. Cheers, Marcos Em Seg, 2013-06-10 às 16:58 -0400, Juan Eugenio Iglesias escreveu:
Hi Marcos,
- your fix of MRIread.m is great. I forgot of this bug; we should
totally take care of it. 2. a problem is that nu.mgz (or norm.mgz) and the posteriors are in difference voxel space. So, you need to resample norm.mgz to the space of the subfields. To do so, you can use mri_convert with the option -rl ("reslice like"): mri_convert norm.mgz norm_resampled.mgz -rl posterior_subiculum.mgz 3. Now you can do: A=MRIread('norm.mgz'); data=A.vol(:); B=MRIread('posterior_subiculum.mgz'); post=double(B.vol(:)); mean=sum(data.*post)/sum(post); variance=sum((data-mean).^2.*post)/sum(post); Cheers, /Eugenio
On Mon, 2013-06-10 at 17:17 -0300, Marcos Martins da Silva wrote:
Hi, Eugenio TY for your fast help. I understood you were suggesting to compute that on MatLab and I tried this:
NU=MRIread('nu.mgz') ### that runs ok Ps=MRIread('posterior_left_subiculum.mgz') ### it fails with the following message WARNING: error reading MR params Attempted to access mr_parms(1); index out of bounds because numel(mr_parms)=0.
Error in MRIread (line 100) tr = mr_parms(1);
I solved creating a little customized nMRIread.m with the following changes:
if numel(mr_parms) > 0 tr = mr_parms(1); flip_angle = mr_parms(2); te = mr_parms(3); ti = mr_parms(4); else mr_parms(1) = 0; mr_parms(2) = 0; mr_parms(3) = 0; mr_parms(4) = 0; tr = mr_parms(1); flip_angle = mr_parms(2); te = mr_parms(3); ti = mr_parms(4); end
With those changes I assigned 0 to mr-parms elements so it runs without errors But whe I tried the following line I copy and pasted from your message: mean=sum(Ps.*NU)/sum(Ps); ### I got the following error Undefined function 'times' for input arguments of type 'struct'.
Any help? Cheers, Marcos
PS: I promised I will post the final solution to list. But I guess it is more didactic if these little problems are solved before. TY, again. Em Seg, 2013-06-10 às 14:19 -0400, Juan Eugenio Iglesias escreveu:
Hi Marcos, the right way of doing this is using the soft posteriors to compute the mean and variance, rather than thresholding at p=0.5. For instance, if you wanted to compute the mean and variance of the intensitites of the subiculum, you would do something like this: mean=sum(Ps.*NU)/sum(Ps); variance=sum(Ps.*(NU-mean).^2)/sum(Ps); (where Ps is the posterior of the subiculum) Cheers, /Eugenio
On Mon, 2013-06-10 at 15:12 -0300, Marcos Martins da Silva wrote:
Hi, Freesurfer Experts
After usual processing with recon-all -all we get the aseg.stats file with several data including intensity values like: normMean normStdDev normMin normMax normRange Left-Hippocampus 77.8939 7.5748 46.0000 105.0000 59.0000
How could I get similar results for each hippocampal subfield, assuming I also generated all posterior*.mgz files corresponding to each subfield? I guess I should first binarize each of the subfields file with a threshold=127 to map all the pertinent voxels, and then use these files as masks over nu.mgz and calculate the intensities values. But I do not know the best way to accomplish this after the binarize step.
Thank you in advance for any help.
Marcos _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Hi, Eugenio Just noticed the first lines of aseg.stats. From the command line recon-all pipeline uses to make the original aseg.stats file it uses norm.mgz so I think it is the best file to use for intensity comparisons like I proposed. Thank you. Marcos Em Qua, 2013-06-12 às 13:33 -0300, Marcos Martins da Silva escreveu:
Hi, Eugenio Hmmm, I guessed the values I got were different because they represented different stages of the processing. And you just confirmed that. Thank you. I would like to compare these intensities with the intensities I can read in aseg.stats. And I guess the comparison would be more interesting if I use the same source. Cheers, Marcos Em Ter, 2013-06-11 às 17:13 -0400, Juan Eugenio Iglesias escreveu:
Dear Marcos, these are just mean and standard deviations of intensities of the images at different levels of processing. What the best measure is depends on what you want to do with them. Cheers, /Eugenio
On Tue, 2013-06-11 at 11:46 -0300, Marcos Martins da Silva wrote:
Hi, Eugenio, TY again for your help. I did the computing without errors after your suggestions. I also computed the intensities based on nu.mgz, norm.mgz and T1.mgz. I would like to know the best measure I should consider, including because I intend to compare these values with intensity data from basal ganglia and cortical areas as I can find in aseg.stats. It follows my results: norm.mgz based meannorm= 78.725 variancenorm = 20.258 (stdev = 4.500888801)
nu.mgz based meannu = 87.362 variancenu = 26.343 (stdev =5.132543229)
T1.mgz based meant1 = 84.023 variancet1 = 23.277 (stdev =4.824624338)
It follows the MatLab/Octave code I used Norm=MRIread('norm_postres.mgz'); datanorm=Norm.vol(:); Ps=nMRIread('posterior_right_subiculum.mgz'); postsub=double(Ps.vol(:)); meannorm=sum(datanorm.*postsub)/sum(postsub); variancenorm=sum((datanorm-meannorm).^2.*postsub)/sum(postsub); Nu=MRIread('nu_postres.mgz'); datanu=Nu.vol(:); meannu=sum(datanu.*postsub)/sum(postsub); variancenu=sum((datanu-meannu).^2.*postsub)/sum(postsub); T1=MRIread('T1_postres.mgz'); datat1=T1.vol(:); meant1=sum(datat1.*postsub)/sum(postsub); variancet1=sum((datat1-meant1).^2.*postsub)/sum(postsub);
The *postres.mgz files are nu, norm and T1 resampled as you suggested. And nMRIread is the workarounded version of MRIread I said in previous message. Cheers, Marcos Em Seg, 2013-06-10 às 16:58 -0400, Juan Eugenio Iglesias escreveu:
Hi Marcos,
- your fix of MRIread.m is great. I forgot of this bug; we should
totally take care of it. 2. a problem is that nu.mgz (or norm.mgz) and the posteriors are in difference voxel space. So, you need to resample norm.mgz to the space of the subfields. To do so, you can use mri_convert with the option -rl ("reslice like"): mri_convert norm.mgz norm_resampled.mgz -rl posterior_subiculum.mgz 3. Now you can do: A=MRIread('norm.mgz'); data=A.vol(:); B=MRIread('posterior_subiculum.mgz'); post=double(B.vol(:)); mean=sum(data.*post)/sum(post); variance=sum((data-mean).^2.*post)/sum(post); Cheers, /Eugenio
On Mon, 2013-06-10 at 17:17 -0300, Marcos Martins da Silva wrote:
Hi, Eugenio TY for your fast help. I understood you were suggesting to compute that on MatLab and I tried this:
NU=MRIread('nu.mgz') ### that runs ok Ps=MRIread('posterior_left_subiculum.mgz') ### it fails with the following message WARNING: error reading MR params Attempted to access mr_parms(1); index out of bounds because numel(mr_parms)=0.
Error in MRIread (line 100) tr = mr_parms(1);
I solved creating a little customized nMRIread.m with the following changes:
if numel(mr_parms) > 0 tr = mr_parms(1); flip_angle = mr_parms(2); te = mr_parms(3); ti = mr_parms(4); else mr_parms(1) = 0; mr_parms(2) = 0; mr_parms(3) = 0; mr_parms(4) = 0; tr = mr_parms(1); flip_angle = mr_parms(2); te = mr_parms(3); ti = mr_parms(4); end
With those changes I assigned 0 to mr-parms elements so it runs without errors But whe I tried the following line I copy and pasted from your message: mean=sum(Ps.*NU)/sum(Ps); ### I got the following error Undefined function 'times' for input arguments of type 'struct'.
Any help? Cheers, Marcos
PS: I promised I will post the final solution to list. But I guess it is more didactic if these little problems are solved before. TY, again. Em Seg, 2013-06-10 às 14:19 -0400, Juan Eugenio Iglesias escreveu:
Hi Marcos, the right way of doing this is using the soft posteriors to compute the mean and variance, rather than thresholding at p=0.5. For instance, if you wanted to compute the mean and variance of the intensitites of the subiculum, you would do something like this: mean=sum(Ps.*NU)/sum(Ps); variance=sum(Ps.*(NU-mean).^2)/sum(Ps); (where Ps is the posterior of the subiculum) Cheers, /Eugenio
On Mon, 2013-06-10 at 15:12 -0300, Marcos Martins da Silva wrote: > Hi, Freesurfer Experts > > After usual processing with recon-all -all we get the aseg.stats file > with several data including intensity values like: > normMean normStdDev > normMin normMax normRange > Left-Hippocampus 77.8939 7.5748 46.0000 > 105.0000 59.0000 > > How could I get similar results for each hippocampal subfield, > assuming I also generated all posterior*.mgz files corresponding to > each subfield? > I guess I should first binarize each of the subfields file with a > threshold=127 to map all the pertinent voxels, and then use these > files as masks over nu.mgz and calculate the intensities values. But I > do not know the best way to accomplish this after the binarize step. > > Thank you in advance for any help. > > Marcos > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
freesurfer@nmr.mgh.harvard.edu
-
Juan Eugenio Iglesias -
Marcos Martins da Silva