[Insight-users] RE: Testing Robustness of Registration

[IsabelleNg]

Hello Olivier,

Thanks for your detailed reply and reference to your paper. My next question
is: how significant/ important is it that the images be sampled with the
same resolution?

For instance, if registering M to F and F, whose resolutions are different,
then how would resampling M to have the same resolution as F before input to
registration affect registration result? My understanding is that this
depends heavily on the interpolation method used, correct? The metric I am
using is MI, but the answer to this question should generalize to other
metrics, right? 

One guideline is that if images are anisotropic (i.e. 0.6 X 0.6 X 3.0mm),
then we should use windowed since interpolators [ITK]. Problem is that they
are too computation-intensive s.t. simpler interpolators should be in the
registration algorithm. Consider the following procedure:

1. Resample F and/or M to istropic images with advanced interpolator I*
(windowed since w/ r=5) s.t. F, M will have same isotropic resolutions.

2. Feed into reg algorithm that uses linear/ nearest interpolation.

3. Resample M using I* with transformation params determined by algm.

How might step 1 improve registration results? (could you discuss this in
relation to the biases/ issues mentioned in your paper if applicable)?

Thanks again,
Isabelle






Olivier Salvado wrote:
> 
> hello Isabelle,
> 
> There is a big difference between the two approaches that you suggest (I
> may
> be wrong because I don't know exactly the itk implementation). If you
> write
> an image at a new location, you will have to interpolate. Since
> interpolation is filtering, you will in fact filter the new image that you
> want to register. Once you find the correct parameters you will
> interpolate
> a second time, thus filtering twice. Whereas by starting with random
> parameters you will not do the first filtering step, only the second.
> 	Results between the two might differ depending on the frequency
> content of your image and the kind of interpolation used.
> 
> 	Another thing you want to consider. is that in real life, even if
> the two images are the same, the noise is different. You should thus add
> different noise after generating two images before trying to register
> them,
> otherwise you will register the noise as much as the image. Since the
> noise
> correlation is very high for zero misregistration, you will get an
> artifactual high maximum at zero on your similarity measure
> (x-correlation,
> mutual information, ...) that will take over any maximum from the image
> content.
> 
> for more details you may want to look at our recent paper on that subject:
> salvado and wilson, "Removal of local and biased global maxima in
> intensity-based registration", Medical Image Analysis April 2007, 11(2). 
> 
> 
> 
> Olivier
> 
> __________________________________________________ 
> Olivier Salvado, PhD
> Case Western Reserve University | Case Center for Imaging Research 
> University Hospitals of Cleveland | Department of Radiology | Wearn B49 
> 11100 Euclid Av. | Cleveland, OH 44106 
> Ph. (216) 983 3426 | Fax: (216) 844 4987
> 
> -----Original Message-----
> From: insight-users-bounces+olivier.salvado=case.edu at itk.org
> [mailto:insight-users-bounces+olivier.salvado=case.edu at itk.org] On Behalf
> Of
> IsabelleNg
> Sent: Friday, May 04, 2007 20:37
> To: insight-users at itk.org
> Subject: [Insight-users] Testing Robustness of Registration
> 
> 
> ITK-users, 
> 
> I wish to test one of the registration algorithm by applying random
> transformations to the moving image. This is often done in papers that
> report registration results as tests of robustness and capture range. Is
> it
> valid to perform these tests by simply initializing the transform with
> random numbers? i..e by calling 
> 
> registration->SetInitialTransformationParameters( randomXform)? 
> 
> Or, do we actually need to physically write out the randomly misaligned
> images and then feed back into the algorithm? 
> 
> How would results differ with these 2 approaches?
> 
> Thanks,
> Isabelle
> 
> 
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