[Insight-users] Parameter scales for registration (second try)

[Nick Tustison]

Not quite.  See below for a relevant block of code.  
The optimizer can take an optional scales estimator.


    typedef itk::RegistrationParameterScalesFromPhysicalShift<MetricType> ScalesEstimatorType;
    typename ScalesEstimatorType::Pointer scalesEstimator = ScalesEstimatorType::New();
    scalesEstimator->SetMetric( singleMetric );
    scalesEstimator->SetTransformForward( true );

    typedef itk::ConjugateGradientLineSearchOptimizerv4 ConjugateGradientDescentOptimizerType;
    typename ConjugateGradientDescentOptimizerType::Pointer optimizer = ConjugateGradientDescentOptimizerType::New();
    optimizer->SetLowerLimit( 0 );
    optimizer->SetUpperLimit( 2 );
    optimizer->SetEpsilon( 0.2 );
    //    optimizer->SetMaximumLineSearchIterations( 20 );
    optimizer->SetLearningRate( learningRate );
    optimizer->SetMaximumStepSizeInPhysicalUnits( learningRate );
    optimizer->SetNumberOfIterations( currentStageIterations[0] );
    optimizer->SetScalesEstimator( scalesEstimator );
    optimizer->SetMinimumConvergenceValue( convergenceThreshold );
    optimizer->SetConvergenceWindowSize( convergenceWindowSize );
    optimizer->SetDoEstimateLearningRateAtEachIteration( this->m_DoEstimateLearningRateAtEachIteration );
    optimizer->SetDoEstimateLearningRateOnce( !this->m_DoEstimateLearningRateAtEachIteration );






On May 7, 2013, at 8:01 AM, Joël Schaerer <joel.schaerer at gmail.com> wrote:

> Hi Nick,
> 
> I did indeed have a look at these new classes (not a very thorough one, I must confess). However if I understand correctly they allow estimating the parameter scales, but don't change the way the scales are used by the optimizer?
> 
> joel
> 
> On 07/05/2013 13:52, Nick Tustison wrote:
>> Hi Brad,
>> 
>> Have you seen the work we did with the class
>> 
>> http://www.itk.org/Doxygen/html/classitk_1_1RegistrationParameterScalesEstimator.html
>> 
>> and it's derived classes for the v4 framework?  They describe
>> a couple different approaches to scaling the gradient for use
>> with the v4 optimizers.
>> 
>> Nick
>> 
>> 
>> On May 7, 2013, at 6:59 AM, Bradley Lowekamp <blowekamp at mail.nih.gov> wrote:
>> 
>>> Hello Joel,
>>> 
>>> I have encountered the same issue. I ended up creating my own "ScaledRegularStepGradientDescentOptimizer" derived from the an ITK one. Please find it attached. Please note, I don't think I have migrated this code to ITKv4.... but I not certain.
>>> 
>>> I reported this issue to the ITKv4 registration team, but I am not sure what happened to it.
>>> 
>>> I also tried to make the change in ITK a while ago, and a large number of the registration tests failed... not sure if the results were better or worse, they were just different.
>>> 
>>> Brad
>>> 
>>> <itkScaledRegularStepGradientDescentOptimizer.h>
>>> 
>>> On Apr 25, 2013, at 11:10 AM, Joël Schaerer <joel.schaerer at gmail.com> wrote:
>>> 
>>> 
>