[Insight-users] Geospatial registration

[Luis Ibanez]

Hi Garrett,

You probably already know this,
but just in case...

ITK optimizers offer a mechanism for adjusting the scale
of cost-function parameters. You pass an array of doubles
to the optimizer and each one of them correspond to a
scaling factor to be used for normalizing the parameters.

You will find examples on the use of the scaling array,
in the ITK Software Guide.

      http://www.itk.org/ItkSoftwareGuide.pdf

in the Registration chapter.



    Regards,


       Luis



---------------------------------
Garrett Potts wrote:
> Hello Luis:
> 
> Thank you for the information.  I'll try the metric for mutual 
> information.  I was currently using MutualInformationImageToImageMetric 
> and out of the metrics I have tried it was giving better results and the 
> only problem that I was having now was the derivative estimates.  For 
> our adjustable parameter interface I think I'll add a scales factor in 
> order to compute derivatives so the change is not so large with respect 
> to each parameter.  For a temporary fix I was dividing the partials by 
> the largest change in pixels and this sort of got the change down to a 
> reasonable rate.
> 
> I'll keep playing.  Thank you again for all the suggestions
> 
> 
> Take care
> 
> Garrett
> 
> 
> 
>> Hi Garrett,
>>
>> Thanks for the detailed description of your project.
>>
>>
>>>> Any suggestions on good metrics and size of the image to analyze let 
>>>> me know.
>>
>>
>>
>> If you are registering multimodality images, your best
>> options for metrics are the variants of Mutual Information.
>> There are about 4 different implementations of Mutual Information
>> in ITK, you probably want to start with MattesMutualInformation,
>> that is one of the smoothest.
>>
>> For a full list of the ImageMetrics available in ITK please look at
>> http://www.itk.org/Insight/Doxygen/html/group__RegistrationMetrics.html
>>
>> Note that you can always write your own customized Image Metric and
>> replace it in the registration framework as you just did with the
>> transform.
>>
>>
>> About the computation of derivatives, the size of the perturbations
>> to be used for computing derivatives by finite differences is something
>> that must be customized based on the dynamic range of every individual
>> parameter, as well as the sensitivity of the transforms to those 
>> particular values.  For example, in a similarity transform, the scale
>> and rotation parameters are very sensitive to small variations.
>>
>> This is one of those issues that can only be solved by experimenting
>> with the values.
>>
>>
>>
> 
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