[Insight-developers] Vector field to Jacobian determinant filter

[Luis Ibanez]

Hi Torsten,

This is great news !,

If we hurry up, we may be able to include your new filter for
the upcoming release 1.10.  We are freezing the CVS repository
on Friday January 21st.


Could you please send us your source code along with a short
line for the credits (author, department, institution).

It will be most helpful and appreciated if you include a test
for the filter. Having a test will expedite the inclusion of
your filter in the repository.

We also need any TABS to be removed from the source code
and replaced with two spaces.

Regarding the alternative option of using this filter
as a measure of gradient magnitude, we usually prefer
to create a new filter with very specific functionality.
This makes documentation and testing much simpler.



Please let us know if you have any questions.


Thanks a lot for contributing your code to ITK.



     Regards,


        Luis




---------------------
Torsten Rohlfing wrote:

> 
> Hi --
> 
> it seems to me that it would be nice to have a filter that takes a 
> vector field (i.e., an n-dimensional image of n-dimensional vectors) and 
> produces an image of the local Jacobian determinants (for deformation 
> morphometry etc). In fact, this was suggested a few weeks back on the 
> itk-users mailing list I believe. As proposed there, I have taken the 
> VectorGradientMagnitudeImageFilter and modified it accordingly. I would 
> be happy to contribute this code if there is any interest.
> 
> Beyond that, I was wondering: one may consider the Jacobian determinant 
> as an alternative measure of gradient magnitude, albeit one that is only 
> well-defined for cases where the dimensionality of the image equals that 
> of the vectors at each pixel. So maybe it would make more sense to 
> include this computation as an alternative operating mode in the 
> VectorGradientMagnitudeImageFilter itself rather than create a new 
> class? The actual code is actually fairly small, thanks to vxl's 
> determinant computation.
> 
> Best,
>  Torsten
> 
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