[Neuroimaging] Analyzing the topology of ROIs and flood-filling in python (skimage?)

[Satrajit Ghosh]

hi ariel,

can you do nearest neighbor interpolation in `mapping.inverse_transform`?
if your original ROI doesn't have holes and you are doing a diffeomorphic
mapping, your target shouldn't have holes either. for a comparison you
could run antsRegister and antsApplyTransforms, with nearest neighbor
interpolation.

cheers,

satra

On Wed, Sep 2, 2015 at 8:13 PM, Ariel Rokem <arokem at gmail.com> wrote:

> Hi everyone,
>
> Jason and I are working on a port of his AFQ system (
> https://github.com/jyeatman/afq) into dipy. We've started sketching out
> some notebooks on how that might work here:
>
> https://github.com/arokem/AFQ-notebooks
>
> The main thrust of this is in this one:
>
>
> https://github.com/arokem/AFQ-notebooks/blob/master/AFQ-registration-callosum.ipynb
>
> The first step in this process is to take a standard ROI of some part of
> the brain (say, corpus callosum, which is where we are starting) and warp
> it into the subject's individual brain through a non-linear registration
> between the individual brain and the template brain on which the ROI was
> defined (in this case MNI152). Registration works phenomenally (see cell
> 17), but because this is a non-linear registration, we find ourselves with
> some holes in the ROI after the transformation (see cell 27 for a
> sum-intensity projects). We are trying to use
> scipy.ndimage.binary_fill_holes to, well, fill these holes, but that
> doesn't seem to be working for us (cell 35 still has that hole...).
>
> Any ideas about what might be going wrong? Are we using fill_holes
> incorrectly? Any other tricks to do flood-filling in python? Should we be
> using skimage?
>
> Thanks!
>
> Ariel
>
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