Yes, well, FSL and ADNI probably borrowed the FWHM concept from SPM. There
is a computer vision theory behind image smoothing which is known as the
scale space theory (http://en.wikipedia.org/wiki/Scale_space) where the
scale is the "variance" of the smoothing kernel (squared "sigma") and is
classically interpreted as a diffusion time. So you don't need to think
about the Gaussian kernel in terms of a probability distribution to have
"sigma" arise as a natural parameter. I've never seen any mention of FWHM
in this literature, nor have I seen any mention of FWHM anywhere beside the
fMRI and VBM literature. For pedantic computer scientists, the FWHM might
be quite an inelegant concept I'm afraid. Of course, I'm not one of them :)


On Wed, Jun 12, 2013 at 10:46 PM, Matthew Brett <notifications@github.com>wrote:

> In nipy/algorithms/registration/histogram_registration.py:
>
> > +    return H
> > +
> > +
> > +def smooth_image(data, affine, sigma):
> > +    """
> > +    Smooth an image by an isotropic Gaussian filter
> > +
> > +    Parameters
> > +    ----------
> > +    data: ndarray
> > +      Image data array
> > +
> > +    affine: ndarray
> > +      Image affine transform
> > +
> > +    sigma: float
>
> Well - SPM, and
>
>    - FSL : http://fsl.fmrib.ox.ac.uk/fsl/fsl4.0/feat5/detail.html#prestats
>    - AFNI :
>    http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dBlurToFWHM.html,
>    http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dmerge.html
>
> I think statisticians don't usually think of sd in spatial terms - I mean
> - the idea of a Gaussian kernel is a bit different when thinking about
> probability distributions and spatial kernels - but I might be wrong.
>
> Your call obviously.
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/nipy/nipy/pull/254/files#r4666570>
> .
>



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