[Insight-users] Much more computation with Itk v4 as compared to v3 ?
Bradley Lowekamp
blowekamp at mail.nih.gov
Fri Oct 26 09:16:38 EDT 2012
Hello lien,
Are you comparing 4.2.1 to 3.20? Or the v3 vs v4 registration in 4.2.1?
Also how does v3 compare to v4 when only a single thread is used?
Thanks for the additional information.
Brad
On Oct 26, 2012, at 9:12 AM, lien lee <lienlee at gmail.com> wrote:
> Thanks for the informative reply, Brian. I am going to double check, and will let you know what I could get.
>
>
> 2012/10/25 brian avants <stnava at gmail.com>
> hi lien
>
> thanks for trying out v4.
>
> i would not give up on it yet as there are many ways to use the v4 framework.
>
> i don't know off hand which is the most computationally efficient
> approach --- to some extent we rely on users to help us understand
> these questions as the number of active developers, right now, is
> relatively few.
>
> typically, if we have questions about efficiency , we use a profiler
> to help understand sources of additional computation.
>
> michael stauffer has done the majority of profiling - and he is cc'd here.
>
> from what i recall, the v4 and v3 metrics are roughly similar in terms
> of speed and per iteration cost for computing the metric,
>
> assuming that you set up the metric in a similar manner.
>
> so , without seeing your v3 code, we cannot understand what
> differences might exist in your v4 and v3 comparison. one obvious
> question is per iteration cost of assessing the metric and how many
> points one is using in the call
>
> vRigidRegistration->SetMetricSamplingPercentage(0.1);
>
> relative to your v3 setup.
>
> the "extra" computation from the virtual domain and in the composite
> transform should be relatively little as the identity transform does
> not require any actual computation.
>
> just a few thoughts ---- perhaps others will have additional feedback.
>
> brian
>
>
>
>
> On Thu, Oct 25, 2012 at 5:20 PM, lien lee <lienlee at gmail.com> wrote:
> > Hi all,
> >
> > As a starting point with v4, a simple rigid transform image registration (as
> > attached at the bottom of this message) was created and tested on a pair of
> > 256x256x187 and 256x256x229 images. It takes about 90s.
> >
> > I had an same registration based on v3, and tested against the same data,
> > but it just took about 12s on the same machine with almost the same matching
> > result.
> >
> > As a newbie, I am not sure whether I did the right thing and I am trying to
> > understand more about v4. By debugging into the v4 code, I noticed that:
> > 1. the new virtual domain (same as the fixed image in my case) introduces
> > one more layer which needs some extra computation.
> > 2. the transformation on a point was done through two Transformxxx()
> > operations by two transform instances in
> > ImageToImageMetricv4::m_CompositeTransform, although one of which is
> > actually an identity transform.
> > and, I am guessing maybe they are reasons for more computing time, but, I am
> > not so sure.
> >
> > Of course, I can just stick to v3, but, I am just curious whether there are
> > some ways that I can avoid those extra computations with v4.
> >
> >
> > //=== Start of the code ===================================
> > //
> > bool
> > RigidTransform(itk::CompositeTransform<double,3>::Pointer vComposite,
> > ImageType const& vFixImage, ImageType const& vMovImage)
> > {
> > //- The Euler transform is a rotation and translation about a center, so
> > we
> > // need to find the rotation center.
> > //
> > typedef itk::Euler3DTransform<double> RigidTransformType;
> > RigidTransformType::Pointer vRigid = RigidTransformType::New();
> > typedef itk::CenteredTransformInitializer< RigidTransformType,
> > ImageType,
> > ImageType >
> > InitializerType;
> > InitializerType::Pointer Initializer = InitializerType::New();
> > Initializer->SetTransform(vRigid);
> > Initializer->SetFixedImage(&vFixImage);
> > Initializer->SetMovingImage(&vMovImage);
> > Initializer->GeometryOn();
> > Initializer->InitializeTransform();
> >
> > vComposite->AddTransform(vRigid);
> >
> > //- Metric
> > //
> > typedef itk::MattesMutualInformationImageToImageMetricv4<ImageType,
> > ImageType> MetricType;
> > MetricType::Pointer vMetric = MetricType::New();
> > vMetric->SetNumberOfHistogramBins(32);
> > vMetric->SetUseFixedImageGradientFilter(false);
> >
> > //- Optimizer
> > //
> > typedef itk::GradientDescentOptimizerv4 OptimizerType;
> > OptimizerType::Pointer vOptimizer = OptimizerType::New();
> > vOptimizer->SetNumberOfIterations( 50 );
> > vOptimizer->SetDoEstimateLearningRateOnce( true );
> > vOptimizer->SetMinimumConvergenceValue( 1e-6 );
> > vOptimizer->SetConvergenceWindowSize( 5 );
> > vOptimizer->SetMaximumStepSizeInPhysicalUnits( 0.5 );
> >
> > //- Scale estimator
> > //
> > itk::OptimizerParameterScalesEstimator::Pointer vScalesEstimator;
> > typedef itk::RegistrationParameterScalesFromJacobian<MetricType>
> > JacobianScalesEstimatorType;
> > {
> > JacobianScalesEstimatorType::Pointer vJacobianScalesEstimator
> > = JacobianScalesEstimatorType::New();
> > vJacobianScalesEstimator->SetMetric(vMetric);
> > vJacobianScalesEstimator->SetTransformForward(true);
> > vScalesEstimator = vJacobianScalesEstimator;
> > }
> > vOptimizer->SetScalesEstimator(vScalesEstimator);
> > vOptimizer->SetDoEstimateScales(true);
> >
> > //- The RegistrationMethod class coordinates the registration operation.
> > // It needs all the pieces that come together to perform the
> > registration
> > // operation.
> > //
> > typedef itk::ImageRegistrationMethodv4<ImageType, ImageType,
> > itk::Euler3DTransform<double>> RigidRegistrationType;
> > RigidRegistrationType::Pointer vRigidRegistration =
> > RigidRegistrationType::New();
> > vRigidRegistration->SetOptimizer(vOptimizer);
> > vRigidRegistration->SetFixedImage(&vFixImage);
> > vRigidRegistration->SetMovingImage(&vMovImage);
> > vRigidRegistration->SetMovingInitialTransform(vRigid);
> > vRigidRegistration->SetNumberOfLevels(3);
> > vRigidRegistration->SetMetric(vMetric);
> >
> > vRigidRegistration->SetMetricSamplingStrategy(RigidRegistrationType::RANDOM);
> > vRigidRegistration->SetMetricSamplingPercentage(0.1);
> >
> > //- Shrink the virtual domain by specified factors for each level.
> > //
> > RigidRegistrationType::ShrinkFactorsArrayType vRigidShrinkFactors;
> > vRigidShrinkFactors.SetSize( 3 );
> > vRigidShrinkFactors[0] = 4;
> > vRigidShrinkFactors[1] = 2;
> > vRigidShrinkFactors[2] = 1;
> > vRigidRegistration->SetShrinkFactorsPerLevel( vRigidShrinkFactors );
> >
> > //- Smoothing sigmas array
> > //
> > RigidRegistrationType::SmoothingSigmasArrayType vRigidSmoothingSigmas;
> > vRigidSmoothingSigmas.SetSize(3);
> > vRigidSmoothingSigmas.Fill(0);
> > vRigidRegistration->SetSmoothingSigmasPerLevel(vRigidSmoothingSigmas);
> >
> > //- Observer
> > //
> > typedef CommandIterationUpdate< RigidRegistrationType > CommandType;
> > CommandType::Pointer observer = CommandType::New();
> > vRigidRegistration->AddObserver( itk::InitializeEvent(), observer );
> >
> > try
> > {
> > std::cout << "Starting rigid registration..." << std::endl;
> > vRigidRegistration->Update();
> > std::cout << "Rigid parameters after registration: " << std::endl
> > << vOptimizer->GetCurrentPosition() << std::endl;
> > }
> > catch( itk::ExceptionObject &e )
> > {
> > std::cerr << "Exception caught: " << e << std::endl;
> > return false;
> > }
> >
> > vComposite->AddTransform(const_cast<RigidTransformType*>(vRigidRegistration->GetOutput()->Get()));
> > return true;
> > }
> > //
> > //=== End of the code =====================================
> >
> >
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>
> Visit other Kitware open-source projects at
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>
> Kitware offers ITK Training Courses, for more information visit:
> http://www.kitware.com/products/protraining.php
>
> Please keep messages on-topic and check the ITK FAQ at:
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