[Insight-users] MattesMutualInformation metric: Fixed and moving image desired features

[Oscar Esteban Sanz-Dranguet]

Hi,

Thanks for your answer :)

When I said "the registration is more quick and accurate" I didn't mean 
the calculation time of every step, I referred to the whole registration 
process. I must say "it takes a very lower number of iterations".

The correct question is, why the Mattes metric has its minimum much more 
defined and diferentiated when using SPECT image as fixed?

Sorry for the previous explanation. Cheers,

Oscar Esteban
BIT - UPM (http://www.die.upm.es/im/)
+34 913 366 827 ext.4248



Karthik Krishnan escribió:
> On Tue, Nov 3, 2009 at 7:12 AM, Oscar Esteban Sanz-Dranguet 
> <oesteban at die.upm.es <mailto:oesteban at die.upm.es>> wrote:
>
>     Hi,
>
>     I'm trying to perform intra-subject rigid registration between MR
>     & SPECT images. I would like the MR to be the fixed image, BUT
>     I've found that if I use the SPECT as fixed, the registration is
>     more quick and accurate. Why does it happen?
>
>
> It should be quicker, using a lower resolution image as the fixed 
> image, for most metrics. The metric is evaluated at each sample point 
> on the fixed image, typically all voxels in the fixed image.
>
> Most metrics sample the whole fixed image. With Mattes, contrary to 
> what you report, one would expect the times to be roughly the same 
> (assuming that you have the same number of samples in both cases).
>
> The final resampling step though, should be a lot faster using the 
> fixed image as the low res image, since the moving image is resampled 
> to the fixed image grid, ie the iterator walks through every pixel in 
> the fixed image and computes the intensity of the moving voxel that 
> resamples onto this location.
>
>  
>
>
>     The images are as follows:
>
>     - SPECT: 128x128x43,
>                  3,32237mmx3,32237mmx3,32237
>                  less than 255 levels of gray
>
>     - MR: 224x240x256
>             0,85mmx0,9mmx0,85mm
>             512 levels of gray
>
>
>     Image preprocessing:
>     - I use a IntensityWindowingImageFilter to rescale the intensities
>     to the range 0,255.
>     - I use a DiscreteGaussianImageFilter with deviation 2.0 to the
>     two images, but I've tried using a lot of different values and
>     combinations. It appears to be the same
>     - I use ScalarImageToHistogramGenerator to find the first maximum
>     of the two images histogram. With it, I use the metric's  member
>     SetFixedImageSamplesIntensityThreshold for the fixed image and I
>     remove the background of the moving image with
>     ThresholdImagefilter. The values used for the two thresholds is
>     the value of the first maximum + 10% of overall frequencies.
>     - Resampling SPECT to MR's spacing and size improves the
>     registration, when using MR as fixed and adequate values of
>     optimizer scales (they are in a very tiny range)
>
>     Initialization:
>     - Using Moments, the SPECT image starts a little lower (10mm) on
>     the Z axis
>
>     Metric:
>     - 70 bins, 10000 samples
>
>     Optimizer:
>     - SPSA or Gradient Descent. I use a visualization pipeline with
>     VTK to test that the scales and the step size make sense.
>
>
>     Thanks in advance. Cheers.
>
>     -- 
>     Oscar Esteban
>     BIT - UPM (http://www.die.upm.es/im/)
>     +34 913 366 827 ext.4248
>
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