ITK  5.0.0
Insight Segmentation and Registration Toolkit
Examples/RegistrationITKv3/DeformableRegistration13.cxx
/*=========================================================================
*
* 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 is almost identical to
// Section~\ref{sec:DeformableRegistration12}, with the difference that it
// illustrates who to use the RegularStepGradientDescentOptimizer for a
// deformable registration task.
//
// \index{itk::BSplineTransform}
// \index{itk::BSplineTransform!DeformableRegistration}
// \index{itk::RegularStepGradientDescentOptimizer}
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The following are the most relevant headers to this example.
//
// \index{itk::BSplineTransform!header}
// \index{itk::RegularStepGradientDescentOptimizer!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// The following section of code implements a Command observer
// used to monitor the evolution of the registration process.
//
#include "itkCommand.h"
class CommandIterationUpdate : public itk::Command
{
public:
using Self = CommandIterationUpdate;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
itkNewMacro( Self );
protected:
CommandIterationUpdate() {};
public:
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
{
OptimizerPointer optimizer = static_cast< OptimizerPointer >( object );
if( !(itk::IterationEvent().CheckEvent( &event )) )
{
return;
}
std::cout << "Iteration : ";
std::cout << optimizer->GetCurrentIteration() << " ";
std::cout << optimizer->GetValue() << " ";
std::cout << 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] ";
std::cerr << " [useExplicitPDFderivatives ] [useCachingBSplineWeights ] ";
std::cerr << " [filenameForFinalTransformParameters] ";
std::cerr << std::endl;
return EXIT_FAILURE;
}
constexpr unsigned int ImageDimension = 2;
using PixelType = unsigned char;
using MovingImageType = itk::Image< PixelType, ImageDimension >;
// Software Guide : BeginLatex
//
// We instantiate now 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. We also intantiate
// the type of the optimizer.
//
// \index{BSplineTransform!New}
// \index{BSplineTransform!Instantiation}
// \index{RegularStepGradientDescentOptimizer!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
FixedImageType,
MovingImageType >;
using InterpolatorType = itk:: LinearInterpolateImageFunction<
MovingImageType,
double >;
using RegistrationType = itk::ImageRegistrationMethod<
FixedImageType,
MovingImageType >;
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
registration->SetInterpolator( interpolator );
TransformType::Pointer transform = TransformType::New();
registration->SetTransform( transform );
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();
FixedImageType::RegionType fixedRegion = fixedImage->GetBufferedRegion();
registration->SetFixedImageRegion( fixedRegion );
unsigned int numberOfGridNodesInOneDimension = 7;
// Software Guide : BeginCodeSnippet
TransformType::PhysicalDimensionsType fixedPhysicalDimensions;
TransformType::MeshSizeType meshSize;
TransformType::OriginType fixedOrigin;
for( unsigned int i=0; i< SpaceDimension; i++ )
{
fixedOrigin[i] = fixedImage->GetOrigin()[i];
fixedPhysicalDimensions[i] = fixedImage->GetSpacing()[i] *
static_cast<double>(
fixedImage->GetLargestPossibleRegion().GetSize()[i] - 1 );
}
meshSize.Fill( numberOfGridNodesInOneDimension - SplineOrder );
transform->SetTransformDomainOrigin( fixedOrigin );
transform->SetTransformDomainPhysicalDimensions(
fixedPhysicalDimensions );
transform->SetTransformDomainMeshSize( meshSize );
transform->SetTransformDomainDirection( fixedImage->GetDirection() );
using ParametersType = TransformType::ParametersType;
const unsigned int numberOfParameters =
transform->GetNumberOfParameters();
ParametersType parameters( numberOfParameters );
parameters.Fill( 0.0 );
transform->SetParameters( parameters );
registration->SetInitialTransformParameters( transform->GetParameters() );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Next we set the parameters of the RegularStepGradientDescentOptimizer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetMaximumStepLength( 10.0 );
optimizer->SetMinimumStepLength( 0.01 );
optimizer->SetRelaxationFactor( 0.7 );
optimizer->SetNumberOfIterations( 200 );
// Software Guide : EndCodeSnippet
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver( itk::IterationEvent(), observer );
metric->SetNumberOfHistogramBins( 50 );
const unsigned int numberOfSamples =
static_cast<unsigned int>( fixedRegion.GetNumberOfPixels() * 60.0 / 100.0 );
metric->SetNumberOfSpatialSamples( numberOfSamples );
// For consistent results when regression testing.
metric->ReinitializeSeed( 121213 );
if( argc > 7 )
{
// Define whether to calculate the metric derivative by explicitly
// computing the derivatives of the joint PDF with respect to the Transform
// parameters, or doing it by progressively accumulating contributions from
// each bin in the joint PDF.
metric->SetUseExplicitPDFDerivatives( std::stoi( argv[7] ) );
}
if( argc > 8 )
{
// Define whether to cache the BSpline weights and indexes corresponding to
// each one of the samples used to compute the metric. Enabling caching will
// make the algorithm run faster but it will have a cost on the amount of memory
// that needs to be allocated. This option is only relevant when using the
// BSplineTransform.
metric->SetUseCachingOfBSplineWeights( std::stoi( argv[8] ) );
}
// Add a time probe
std::cout << std::endl << "Starting Registration" << std::endl;
try
{
memorymeter.Start( "Registration" );
chronometer.Start( "Registration" );
registration->Update();
chronometer.Stop( "Registration" );
memorymeter.Stop( "Registration" );
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;
}
OptimizerType::ParametersType finalParameters =
registration->GetLastTransformParameters();
// Report the time and memory taken by the registration
chronometer.Report( std::cout );
memorymeter.Report( std::cout );
transform->SetParameters( finalParameters );
using ResampleFilterType = itk::ResampleImageFilter<
MovingImageType,
FixedImageType >;
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform( transform );
resample->SetInput( movingImageReader->GetOutput() );
resample->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
resample->SetOutputOrigin( fixedImage->GetOrigin() );
resample->SetOutputSpacing( fixedImage->GetSpacing() );
resample->SetOutputDirection( fixedImage->GetDirection() );
// This value is set to zero in order to make easier to perform
// regression testing in this example. However, for didactic
// exercise it will be better to set it to a medium gray value
// such as 100 or 128.
resample->SetDefaultPixelValue( 0 );
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 > 4 )
{
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 > 5 )
{
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.
if( argc > 6 )
{
using DisplacementFieldType = itk::Image< VectorType, ImageDimension >;
DisplacementFieldType::Pointer field = DisplacementFieldType::New();
field->SetRegions( fixedRegion );
field->SetOrigin( fixedImage->GetOrigin() );
field->SetSpacing( fixedImage->GetSpacing() );
field->SetDirection( fixedImage->GetDirection() );
field->Allocate();
FieldIterator fi( field, fixedRegion );
fi.GoToBegin();
TransformType::InputPointType fixedPoint;
TransformType::OutputPointType movingPoint;
VectorType displacement;
while( ! fi.IsAtEnd() )
{
index = fi.GetIndex();
field->TransformIndexToPhysicalPoint( index, fixedPoint );
movingPoint = transform->TransformPoint( fixedPoint );
displacement = movingPoint - fixedPoint;
fi.Set( displacement );
++fi;
}
FieldWriterType::Pointer fieldWriter = FieldWriterType::New();
fieldWriter->SetInput( field );
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;
}
}
// Optionally, save the transform parameters in a file
if( argc > 9 )
{
std::ofstream parametersFile;
parametersFile.open( argv[9] );
parametersFile << finalParameters << std::endl;
parametersFile.close();
}
return EXIT_SUCCESS;
}