ITK  5.0.0
Insight Segmentation and Registration Toolkit
Examples/RegistrationITKv4/DeformableRegistration6.cxx
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*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
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// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{BSplineTransform}
// class in a multi-resolution scheme. Here we run 3 levels of resolutions.
// The first level of registration is performed with the spline grid of
// low resolution. Then, a common practice is to increase the resolution
// of the B-spline mesh (or, analogously, the control point grid size)
// at each level.
//
// For this purpose, we introduce the concept of transform adaptors.
// Each level of each stage is defined by a transform adaptor
// which describes how to adapt the transform to the current level by
// increasing the resolution from the previous level.
// Here, we used \doxygen{BSplineTransformParametersAdaptor} class
// to adapt the BSpline transform parameters at each resolution level.
// Note that for many transforms, such as affine, the
// concept of an adaptor may be nonsensical since the number of transform
// parameters does not change between resolution levels.
//
// This examples use the \doxygen{LBFGS2Optimizerv4}, which is the new
// implementation of the quasi-Newtown unbounded limited-memory
// Broyden Fletcher Goldfarb Shannon (LBFGS) optimizer. The unbounded
// version does not require specification of the bounds of the
// parameters space, since the number of parameters change at each
// B-Spline resolution this implementation is preferred.
//
// Since this example is quite similar to the previous example on the use
// of the \code{BSplineTransform} we omit most of the details already
// discussed and will focus on the aspects related to the multi-resolution
// approach.
//
// \index{itk::BSplineTransform}
// \index{itk::BSplineTransform!DeformableRegistration}
// \index{itk::LBFGS2Optimizerv4}
// \index{itk::BSplineTransformParametersAdaptor}
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// We include the header files for the transform, optimizer and adaptor.
//
// \index{itk::BSplineTransform!header}
// \index{itk::LBFGS2Optimizer!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
class CommandIterationUpdate : public itk::Command
{
public:
using Self = CommandIterationUpdate;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
itkNewMacro( Self );
protected:
CommandIterationUpdate() = default;
public:
using OptimizerType = itk::LBFGS2Optimizerv4;
using OptimizerPointer = const OptimizerType*;
void Execute(itk::Object *caller, const itk::EventObject & event) override
{
Execute( (const itk::Object *)caller, event);
}
void Execute(const itk::Object * object, const itk::EventObject & event) override
{
auto optimizer = static_cast< OptimizerPointer >( object );
if( ! itk::IterationEvent().CheckEvent( &event ) )
{
return;
}
std::cout << optimizer->GetCurrentIteration() << " = ";
std::cout << optimizer->GetValue() << std::endl;
std::cout << "\t" << optimizer->GetCurrentGradientNorm()
<< " " << optimizer->GetCurrentParameterNorm()
<< " " << optimizer->GetCurrentStepSize() << std::endl;
}
};
int main( int argc, char *argv[] )
{
if( argc < 4 )
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " fixedImageFile movingImageFile outputImagefile ";
std::cerr << " [differenceOutputfile] [differenceBeforeRegistration] ";
std::cerr << " [deformationField] ";
return EXIT_FAILURE;
}
constexpr unsigned int ImageDimension = 2;
using PixelType = float;
using MovingImageType = itk::Image< PixelType, ImageDimension >;
// Software Guide : BeginLatex
//
// We instantiate the type of the \code{BSplineTransform} using
// as template parameters the type for coordinates representation, the
// dimension of the space, and the order of the BSpline.
//
// \index{BSplineTransform!New}
// \index{BSplineTransform!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int SpaceDimension = ImageDimension;
constexpr unsigned int SplineOrder = 3;
using CoordinateRepType = double;
using TransformType = itk::BSplineTransform<
CoordinateRepType,
SpaceDimension,
SplineOrder >;
// Software Guide : EndCodeSnippet
using OptimizerType = itk::LBFGS2Optimizerv4;
FixedImageType,
MovingImageType >;
using RegistrationType = itk::ImageRegistrationMethodv4<
FixedImageType,
MovingImageType >;
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
using FixedImageReaderType = itk::ImageFileReader< FixedImageType >;
using MovingImageReaderType = itk::ImageFileReader< MovingImageType >;
FixedImageReaderType::Pointer fixedImageReader = FixedImageReaderType::New();
MovingImageReaderType::Pointer movingImageReader = MovingImageReaderType::New();
fixedImageReader->SetFileName( argv[1] );
movingImageReader->SetFileName( argv[2] );
FixedImageType::ConstPointer fixedImage = fixedImageReader->GetOutput();
registration->SetFixedImage( fixedImage );
registration->SetMovingImage( movingImageReader->GetOutput() );
fixedImageReader->Update();
// Software Guide : BeginLatex
//
// We construct the transform object, initialize its parameters and
// connect that to the registration object.
//
// \index{itk::RegistrationMethod!SetTransform()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer outputBSplineTransform = TransformType::New();
// Initialize the fixed parameters of transform (grid size, etc).
//
using InitializerType = itk::BSplineTransformInitializer<
TransformType,
FixedImageType>;
InitializerType::Pointer transformInitializer = InitializerType::New();
unsigned int numberOfGridNodesInOneDimension = 8;
TransformType::MeshSizeType meshSize;
meshSize.Fill( numberOfGridNodesInOneDimension - SplineOrder );
transformInitializer->SetTransform( outputBSplineTransform );
transformInitializer->SetImage( fixedImage );
transformInitializer->SetTransformDomainMeshSize( meshSize );
transformInitializer->InitializeTransform();
// Set transform to identity
//
using ParametersType = TransformType::ParametersType;
const unsigned int numberOfParameters =
outputBSplineTransform->GetNumberOfParameters();
ParametersType parameters( numberOfParameters );
parameters.Fill( 0.0 );
outputBSplineTransform->SetParameters( parameters );
registration->SetInitialTransform( outputBSplineTransform );
registration->InPlaceOn();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The registration process is run in three levels. The shrink factors
// and smoothing sigmas are set for each level.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
constexpr unsigned int numberOfLevels = 3;
RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
shrinkFactorsPerLevel.SetSize( numberOfLevels );
shrinkFactorsPerLevel[0] = 3;
shrinkFactorsPerLevel[1] = 2;
shrinkFactorsPerLevel[2] = 1;
RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
smoothingSigmasPerLevel.SetSize( numberOfLevels );
smoothingSigmasPerLevel[0] = 2;
smoothingSigmasPerLevel[1] = 1;
smoothingSigmasPerLevel[2] = 0;
registration->SetNumberOfLevels( numberOfLevels );
registration->SetSmoothingSigmasPerLevel( smoothingSigmasPerLevel );
registration->SetShrinkFactorsPerLevel( shrinkFactorsPerLevel );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Create the transform adaptors to modify the flexibility
// of the deformable transform for each level of this
// multi-resolution scheme.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
RegistrationType::TransformParametersAdaptorsContainerType adaptors;
// First, get fixed image physical dimensions
TransformType::PhysicalDimensionsType fixedPhysicalDimensions;
for( unsigned int i=0; i< SpaceDimension; i++ )
{
fixedPhysicalDimensions[i] = fixedImage->GetSpacing()[i] *
static_cast<double>(
fixedImage->GetLargestPossibleRegion().GetSize()[i] - 1 );
}
// Create the transform adaptors specific to B-splines
for( unsigned int level = 0; level < numberOfLevels; level++ )
{
using ShrinkFilterType = itk::ShrinkImageFilter<
FixedImageType,
FixedImageType>;
ShrinkFilterType::Pointer shrinkFilter = ShrinkFilterType::New();
shrinkFilter->SetShrinkFactors( shrinkFactorsPerLevel[level] );
shrinkFilter->SetInput( fixedImage );
shrinkFilter->Update();
// A good heuristic is to double the b-spline mesh resolution at each level
//
TransformType::MeshSizeType requiredMeshSize;
for( unsigned int d = 0; d < ImageDimension; d++ )
{
requiredMeshSize[d] = meshSize[d] << level;
}
using BSplineAdaptorType =
BSplineAdaptorType::Pointer bsplineAdaptor = BSplineAdaptorType::New();
bsplineAdaptor->SetTransform( outputBSplineTransform );
bsplineAdaptor->SetRequiredTransformDomainMeshSize( requiredMeshSize );
bsplineAdaptor->SetRequiredTransformDomainOrigin(
shrinkFilter->GetOutput()->GetOrigin() );
bsplineAdaptor->SetRequiredTransformDomainDirection(
shrinkFilter->GetOutput()->GetDirection() );
bsplineAdaptor->SetRequiredTransformDomainPhysicalDimensions(
fixedPhysicalDimensions );
adaptors.push_back( bsplineAdaptor );
}
registration->SetTransformParametersAdaptorsPerLevel( adaptors );
// Software Guide : EndCodeSnippet
// Scale estimator
ScalesEstimatorType::Pointer scalesEstimator = ScalesEstimatorType::New();
scalesEstimator->SetMetric( metric );
scalesEstimator->SetTransformForward( true );
scalesEstimator->SetSmallParameterVariation( 1.0 );
// Set Optimizer
optimizer->SetScalesEstimator( scalesEstimator );
optimizer->SetSolutionAccuracy( 1e-4 );
optimizer->SetHessianApproximationAccuracy( 5 );
optimizer->SetMaximumIterations( 100 );
optimizer->SetMaximumLineSearchEvaluations( 10 );
// Connect an observer
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver( itk::IterationEvent(), observer );
std::cout << "Starting Registration "
<< std::endl;
try
{
registration->Update();
std::cout << "Optimizer stop condition = "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
// Finally we use the last transform in order to resample the image.
//
using ResampleFilterType = itk::ResampleImageFilter<
MovingImageType,
FixedImageType >;
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform( outputBSplineTransform );
resample->SetInput( movingImageReader->GetOutput() );
resample->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
resample->SetOutputOrigin( fixedImage->GetOrigin() );
resample->SetOutputSpacing( fixedImage->GetSpacing() );
resample->SetOutputDirection( fixedImage->GetDirection() );
resample->SetDefaultPixelValue( 100 );
using OutputPixelType = unsigned char;
using CastFilterType = itk::CastImageFilter<
FixedImageType,
OutputImageType >;
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
writer->SetFileName( argv[3] );
caster->SetInput( resample->GetOutput() );
writer->SetInput( caster->GetOutput() );
try
{
writer->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
using DifferenceFilterType = itk::SquaredDifferenceImageFilter<
FixedImageType,
FixedImageType,
OutputImageType >;
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
WriterType::Pointer writer2 = WriterType::New();
writer2->SetInput( difference->GetOutput() );
// Compute the difference image between the
// fixed and resampled moving image.
if( argc >= 5 )
{
difference->SetInput1( fixedImageReader->GetOutput() );
difference->SetInput2( resample->GetOutput() );
writer2->SetFileName( argv[4] );
try
{
writer2->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
}
// Compute the difference image between the
// fixed and moving image before registration.
if( argc >= 6 )
{
writer2->SetFileName( argv[5] );
difference->SetInput1( fixedImageReader->GetOutput() );
difference->SetInput2( movingImageReader->GetOutput() );
try
{
writer2->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
}
// Generate the explicit deformation field resulting from
// the registration.
using VectorPixelType = itk::Vector< float, ImageDimension >;
using DisplacementFieldImageType = itk::Image< VectorPixelType, ImageDimension >;
using DisplacementFieldGeneratorType = itk::TransformToDisplacementFieldFilter<
DisplacementFieldImageType,
CoordinateRepType >;
DisplacementFieldGeneratorType::Pointer dispfieldGenerator =
DisplacementFieldGeneratorType::New();
dispfieldGenerator->UseReferenceImageOn();
dispfieldGenerator->SetReferenceImage( fixedImage );
dispfieldGenerator->SetTransform( outputBSplineTransform );
try
{
dispfieldGenerator->Update();
}
catch ( itk::ExceptionObject & err )
{
std::cerr << "Exception detected while generating deformation field";
std::cerr << " : " << err << std::endl;
return EXIT_FAILURE;
}
FieldWriterType::Pointer fieldWriter = FieldWriterType::New();
fieldWriter->SetInput( dispfieldGenerator->GetOutput() );
if( argc >= 7 )
{
fieldWriter->SetFileName( argv[6] );
try
{
fieldWriter->Update();
}
catch( itk::ExceptionObject & excp )
{
std::cerr << "Exception thrown " << std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
}
return EXIT_SUCCESS;
}