Oh yeah - I forgot about that issue. Do I understand correctly that we
simply divide the number we get by 4n to correct for the bias?


On Thu, Jan 31, 2013 at 1:18 PM, Bob Dougherty <notifications@github.com>wrote:

> In mrDiffusion/utils/dtiComputeImageNoise.m:
>
> > +if strcmp('corner', noiseCalcMethod)
> > +
> > +    % According to Henkelman (1985), the expected signal variance (sigma) can
> > +    % be computed as 1.5267 * SD of the background (thermal) noise.
> > +    sz = size(dwRaw.data);
> > +    x  = 10;
> > +    y  = 10;
> > +    z  = round(sz(3)/2);
> > +
> > +    [x,y,z,s] = ndgrid(x-5:x+5, y-5:y:5, z-5:z+5, 1:sz(4));
> > +    noiseInds = sub2ind(sz, x(:), y(:), z(:), s(:));
> > +    sigma     = 1.5267 * std(double(dwRaw.data(noiseInds)));
> > +
> > +elseif strcmp('b0', noiseCalcMethod)
> > +    % Pull out the data from the brain mask
> > +    data =
>
> NIce! But don't we need to correct the small-n bias in the estimated
> stdev? E.g.: http://nbviewer.ipython.org/4287207/
> … <#13c92798c7514325_>
> On 01/31/2013 12:16 PM, Ariel Rokem wrote: In
> mrDiffusion/utils/dtiComputeImageNoise.m: > +if strcmp('corner',
> noiseCalcMethod) > + > + % According to Henkelman (1985), the expected
> signal variance (sigma) can > + % be computed as 1.5267 * SD of the
> background (thermal) noise. > + sz = size(dwRaw.data); > + x = 10; > + y =
> 10; > + z = round(sz(3)/2); > + > + [x,y,z,s] = ndgrid(x-5:x+5, y-5:y:5,
> z-5:z+5, 1:sz(4)); > + noiseInds = sub2ind(sz, x(:), y(:), z(:), s(:)); > +
> sigma = 1.5267 * std(double(dwRaw.data(noiseInds))); > + > +elseif
> strcmp('b0', noiseCalcMethod) > + % Pull out the data from the brain mask >
> + data = Excellent! Maybe @rfdougherty <https://github.com/rfdougherty> <
> https://github.com/rfdougherty> can have a look when he gets a chance, to
> see that this is not crazy. … <#> On Thu, Jan 31, 2013 at 12:11 PM, Jason
> D. Yeatman <notifications@github.com > wrote: > In
> mrDiffusion/utils/dtiComputeImageNoise.m: > > > +if strcmp('corner',
> noiseCalcMethod) > > + > > + % According to Henkelman (1985), the expected
> signal variance (sigma) can > > + % be computed as 1.5267 * SD of the
> background (thermal) noise. > > + sz = size(dwRaw.data); > > + x = 10; > >
> + y = 10; > > + z = round(sz(3)/2); > > + > > + [x,y,z,s] = ndgrid(x-5:x+5,
> y-5:y:5, z-5:z+5, 1:sz(4)); > > + noiseInds = sub2ind(sz, x(:), y(:), z(:),
> s(:)); > > + sigma = 1.5267 * std(double(dwRaw.data(noiseInds))); > > + > >
> +elseif strcmp('b0', noiseCalcMethod) > > + % Pull out the data from the
> brain mask > > + data = > > Yes this part has not been finished. But the
> robust tesnor fit function > works. It just needs to be modularized > …
> <#13c923c0fde51498_> > On Thu, Jan 31, 2013 at 12:07 PM, Ariel Rokem <
> notifications@github.com>wrote: In
> mrDiffusion/utils/dtiComputeImageNoise.m: > +if strcmp('corner',
> noiseCalcMethod) > + > + % According to Henkelman (1985), the expected
> signal variance (sigma) can > + % be computed as 1.5267 * SD of the
> background (thermal) noise. > + sz = size(dwRaw.data); > + x = 10; > + y =
> 10; > + z = round(sz(3)/2); > + > + [x,y,z,s] = ndgrid(x-5:x+5, y-5:y:5,
> z-5:z+5, 1:sz(4)); > + noiseInds = sub2ind(sz, x(:), y(:), z(:), s(:)); > +
> sigma = 1.5267 * std(double(dwRaw.data(noiseInds))); > + > +elseif
> strcmp('b0', noiseCalcMethod) > + % Pull out the data from the brain mask >
> + data = This is still broken, no? — Reply to this email directly or view
> it on GitHub<https://github.com/vistalab/vistasoft/pull/4/files#r2850004>.
> — Reply to this email directly or view it on GitHub<
> https://github.com/vistalab/vistasoft/pull/4/files#r2850052>. — Reply to
> this email directly or view it on GitHub <
> https://github.com/vistalab/vistasoft/pull/4/files#r2850129>.
>  -- Robert F. Dougherty, PhD Research Director Stanford Center for
> Cognitive and Neurobiological Imaging 70 Jordan Hall * Stanford CA 94305 *
> 650-725-0051 http://www.stanford.edu/~bobd
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/vistalab/vistasoft/pull/4/files#r2851095>.
>
>