Hello Nick,
Thanks for getting back to me on this.
I also ran the Sinc interpolators on a constant image, and I got a larger error
than expected. The following Python code was run with the ZeroFlux boundary
condition and the radius template parameter of 5:
# Create a image of all ones
img = sitk.Image( 10, 10 , sitk.sitkFloat64 )
img += 1
iterps = [sitk.sitkNearestNeighbor,
sitk.sitkLinear,
sitk.sitkBSpline,
sitk.sitkGaussian,
sitk.sitkHammingWindowedSinc,
sitk.sitkCosineWindowedSinc,
sitk.sitkWelchWindowedSinc,
sitk.sitkLanczosWindowedSinc,
sitk.sitkBlackmanWindowedSinc]
for i in iterps:
eimg= sitk.Expand( img, [10,10], i )
print "RMS:",(sum( (1-eimg)**2)/len(eimg))**.5, "Abs:",
max(sitk.Abs(1-eimg))
RMS: 0.0 Abs: 0.0
RMS: 0.0 Abs: 0.0
RMS: 1.90178968104e-16 Abs: 5.55111512313e-16
RMS: 0.0 Abs: 0.0
RMS: 0.00519432546396 Abs: 0.007170554427
RMS: 0.00111584107245 Abs: 0.00190357704047
RMS: 0.000697067283848 Abs: 0.00118879124085
RMS: 0.00143647177089 Abs: 0.00245097611656
RMS: 0.000491351024756 Abs: 0.000833429218405
Skimming through the code it looks like the kernel is point sampled and not
integrated over the pixel. I wonder if that is the issue.
Brad
On Dec 18, 2012, at 3:03 PM, Nicholas Tustison <[email protected]> wrote:
> 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 <[email protected]> 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
>
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