[Insight-users] deformable registration in binary image

[Luis Ibanez]

Hi Roger,


Your modifications to the code seem to be reasonable.

The main difference between the examples


      DeformableRegistration7

and

      DeformableRegistration8


is that the first one is intended for images of the
same modality, while the second is intended for images
of different modality.

Given that you replaced the Metric with the MatchCardinality
metric, then there is no major difference left between the
two examples.


I'm not sure about the problem you find with the
infinity norm of the projected gradient,.. but if
you are using the LBFGSB optimizer, it is known
that it has some issues if you start it on a local
minimum (maximum), in other words, if you start the
optimization problem in a place where the derivative
of the metric is null, then the optimizer can not
complete the first iteration.


Please give us more details on how you are initializing
the Transform, and how similar the Fixed and Moving
images are.


    Thanks



       Luis






--------------------
Roger Alvaredo wrote:
> Hi!
>  
> I tested  deformableRegistration7.cxx or deformableRegistration8.cxx in 
> MRI's and they work correctly. Now I would like to re adapt def8 for 
> working with binary images.
>  
> I change the metric to MatcthCardinality and the interpolator to 
> NearestNeighbor.
>  
> In the case of the metric I add the method:  metric->MeasureMatchesOff() 
> because the optimizer minimizes the metric, and I remove the lines of 
> MattesMutualInformation as the number of histograms and so on. It is ok??
>  
> So, I would like to know...:
>  
> 1. Advantatges and disvantatges of each one (def7 and def8).
> 2. I have a problem because the change of metric...:
> The initial values are: GetValue()= 0.0709776 
> and GetInfinityNormOfProjectedGradient()=0
> why is 0 the norm? it stops before doing an iteration for this...
>  
> Any advice?
>  
> Thanks!
>  
>  
> 
> 
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