[Insight-users] itkStreamingImageFilter with itkHessianSmoothed3DToVesselnessMeasureImageFilter

[Dan Mueller]

Hi Karthik,

I too found the speed somewhat disappointing, although I'm not sure
how else it can be optimized.

Basically the new DiscreteHessianGaussianImageFunction computes a
kernel (not included in timings reported) for a
NeighborhoodOperatorImageFunction: the bigger the kernel, the slower
the function. I guess the trick is to work out the minimum acceptable
error for your specific application, and construct the smallest kernel
within that error.

For the simple test I reported the kernel image was of size [13, 13,
13]. If this could be reduced for your application, then the function
would run faster...

Regards, Dan

2008/11/5 Karthik Krishnan <karthik.krishnan at kitware.com>:
> On Wed, Nov 5, 2008 at 4:14 AM, Dan Mueller <dan.muel at gmail.com> wrote:
>> Hi Iván,
>>
>>> Could someone also test the performance?
>>
>> I put together a simple program (see below) to test the performance of
>> DiscreteHessianGaussianImageFunction (the test image is in the ITK
>> Testing\Data\Input folder). As you assumed the recursive approach is
>> much faster:
>>
>> HessianTest HeadMRVolumeCompressed.mha
>> Filter took 0.0792656 seconds
>> Function took 17.0815 seconds
>>
>> Image Size = [48, 62, 42]
>> Image Spacing = [4.0, 4.0, 4.0]
>> Sigma = 1.0
>> MaxKernelWidth = 32
>> MaximumError = 0.01
>> NormalizeAcrossScale = false
>> UseImageSpacing = false
>>
>> Speed Factor = 17.08 / 0.079 = 216
>>
>> (Windows Vista SP1, Visual Studio 2005, Release build, Intel Core 2
>> Duo 2GHz, 3 GB RAM)
>>
>> So to make the new function pay off (in terms of speed) it seems a
>> user will have to restrict the computation to an image region ~1/250
>> of the whole input image. For my use cases this is a certainty, but I
>> can image for some (most?) this will not be the case.
>
> Thanks a lot Dan. This information is very helpful. It would be great if you
> could add it as a review to the paper:
>  http://www.insight-journal.org/browse/publication/179
>
> A factor of 250 is rather steep. I was hoping it'd be a x10 slowdown,
> which would
> work well for me. Having a function is very convenient since I can compute
> the hessian on a masked image, which is what I need.
>
> Ivan: Could you please tell us if you expected a factor of 250 slowdown ?
>
> Thanks
> --
> karthik
>