[Insight-developers] Default boundary condition for WindowedSinc as constant?

[Nicholas Tustison]

Hi Brad,

Yeah, we just use the default.  We've probably never noticed it since,
as you say, we typically are interpolating a blob in the middle of a black
background.

I think Paul Yushkevich wrote those windowed sinc interpolators.  You 
might want to ask him why they're the default.

Nick



On Dec 18, 2012, at 1:09 PM, Bradley Lowekamp <blowekamp at mail.nih.gov> wrote:

> Hello,
> 
> As I am finally integrating the different interpolators into SimpleITK. I am giving them a close look over.
> 
> The set of WindowSincInterpolateImageFunctions takes a Boundary condition template parameter. This defaults to ConstantBoundaryCondition. That is by default the pixels are zero outside the image, and they are not zero flux. This results in quite a bit of ringing and fading around my test images. It seems just wrong.
> 
> I can easily specify this parameter as the ZeroFluxNeumannBoundaryCondition (I don't think we have a mirror/reflective boundary, which is another possibility), and things look quite good and as I expect the output to be. I was curious as to what others were doing so I perused BRAINS and ANTS, grepping for the sinc interpolator. And to my surprise they are using the default!
> 
> Is there a reason that this default is preferred? Or is it that I am not processing a single blob in the center of a black image (aka a brain)?
> 
> Also in terms of consistency across the interpolators, this is the only one which takes a boundary condition template parameters. The other interpolators appear to behave sensibly, and exhibit a zero-flux type boundary condition. I think the default for this may need to be changed.
> 
> 
> I have this little example I have been working on in SimpleITK with the famed cthead1.png data input. Here is a code snippet:
> 
> 
> image = image[(size[0]//2-25):(size[0]//2+25),(size[1]//2-25):(size[1]//2+25)]
> 
> 
> iterps = [sitk.sitkNearestNeighbor,
>          sitk.sitkLinear,
>          sitk.sitkBSpline,
>          sitk.sitkGaussian,
>          sitk.sitkHammingWindowedSinc,
>          sitk.sitkCosineWindowedSinc,
>          sitk.sitkWelchWindowedSinc,
>          sitk.sitkLanczosWindowedSinc,
>          sitk.sitkBlackmanWindowedSinc]
> 
> eFactor=5
> 
> image_list = []
> 
> for i in iterps:
>    image_list.append( sitk.Expand( image, [eFactor]*3, i ))
> 
> tiles = sitk.Tile( image_list, [3,0] )
> 
> And the following is the output with the different boundary conditions:
> 
> http://erie.nlm.nih.gov/~blowek1/images/expand_interp_cbc.png
> http://erie.nlm.nih.gov/~blowek1/images/expand_interp_zfbc.png
> 
> Thanks for you feedback,
> Brad