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,
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 
> > > _______________________________________________
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> > > Freesurfer@nmr.mgh.harvard.edu
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> > 
>