ITK  5.0.0
Insight Segmentation and Registration Toolkit
Examples/RegistrationITKv3/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 manually controlled multi-resolution scheme. Here we define two
// transforms at two different resolution levels. A first registration is
// performed with the spline grid of low resolution, and the results are then
// used for initializing a higher resolution grid. Since this example is quite
// similar to the previous example on the use of the
// \code{BSplineTransform} we omit here 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::LBFGSOptimizer}
//
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// We include the header files for the transform and the optimizer.
//
// \index{itk::BSplineTransform!header}
// \index{itk::LBFGSOptimizer!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// NOTE: the LBFGSOptimizer does not invoke events
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 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.
//
// \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::LBFGSOptimizer;
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 );
// Software Guide : BeginLatex
//
// We construct two transform objects, each one will be configured for a resolution level.
// Notice than in this multi-resolution scheme we are not modifying the
// resolution of the image, but rather the flexibility of the deformable
// transform itself.
//
// \index{itk::RegistrationMethod!SetTransform()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer transformLow = TransformType::New();
registration->SetTransform( transformLow );
// Software Guide : EndCodeSnippet
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 numberOfGridNodes = 8;
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( numberOfGridNodes - SplineOrder );
transformLow->SetTransformDomainOrigin( fixedOrigin );
transformLow->SetTransformDomainPhysicalDimensions(
fixedPhysicalDimensions );
transformLow->SetTransformDomainMeshSize( meshSize );
transformLow->SetTransformDomainDirection( fixedImage->GetDirection() );
using ParametersType = TransformType::ParametersType;
const unsigned int numberOfParameters =
transformLow->GetNumberOfParameters();
ParametersType parametersLow( numberOfParameters );
parametersLow.Fill( 0.0 );
transformLow->SetParameters( parametersLow );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We now pass the parameters of the current transform as the initial
// parameters to be used when the registration process starts.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetInitialTransformParameters( transformLow->GetParameters() );
optimizer->SetGradientConvergenceTolerance( 0.05 );
optimizer->SetLineSearchAccuracy( 0.9 );
optimizer->SetDefaultStepLength( 1.5 );
optimizer->TraceOn();
optimizer->SetMaximumNumberOfFunctionEvaluations( 1000 );
std::cout << "Starting Registration with low resolution transform"
<< 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;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Once the registration has finished with the low resolution grid, we
// proceed to instantiate a higher resolution
// \code{BSplineTransform}.
//
// Software Guide : EndLatex
TransformType::Pointer transformHigh = TransformType::New();
numberOfGridNodes = 12;
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( numberOfGridNodes - SplineOrder );
transformHigh->SetTransformDomainOrigin( fixedOrigin );
transformHigh->SetTransformDomainPhysicalDimensions(
fixedPhysicalDimensions );
transformHigh->SetTransformDomainMeshSize( meshSize );
transformHigh->SetTransformDomainDirection( fixedImage->GetDirection() );
ParametersType parametersHigh( transformHigh->GetNumberOfParameters() );
parametersHigh.Fill( 0.0 );
// Software Guide : BeginLatex
//
// Now we need to initialize the BSpline coefficients of the higher resolution
// transform. This is done by first computing the actual deformation field
// at the higher resolution from the lower resolution BSpline coefficients.
// Then a BSpline decomposition is done to obtain the BSpline coefficient of
// the higher resolution transform.
//
// Software Guide : EndLatex
unsigned int counter = 0;
for ( unsigned int k = 0; k < SpaceDimension; k++ )
{
using ParametersImageType = TransformType::ImageType;
ResamplerType::Pointer upsampler = ResamplerType::New();
FunctionType::Pointer function = FunctionType::New();
using IdentityTransformType = itk::IdentityTransform<double,SpaceDimension>;
IdentityTransformType::Pointer identity = IdentityTransformType::New();
upsampler->SetInput( transformLow->GetCoefficientImages()[k] );
upsampler->SetInterpolator( function );
upsampler->SetTransform( identity );
upsampler->SetSize( transformHigh->GetCoefficientImages()[k]->
GetLargestPossibleRegion().GetSize() );
upsampler->SetOutputSpacing(
transformHigh->GetCoefficientImages()[k]->GetSpacing() );
upsampler->SetOutputOrigin(
transformHigh->GetCoefficientImages()[k]->GetOrigin() );
upsampler->SetOutputDirection( fixedImage->GetDirection() );
using DecompositionType =
DecompositionType::Pointer decomposition = DecompositionType::New();
decomposition->SetSplineOrder( SplineOrder );
decomposition->SetInput( upsampler->GetOutput() );
decomposition->Update();
ParametersImageType::Pointer newCoefficients = decomposition->GetOutput();
// copy the coefficients into the parameter array
Iterator it( newCoefficients,
transformHigh->GetCoefficientImages()[k]->GetLargestPossibleRegion() );
while ( !it.IsAtEnd() )
{
parametersHigh[ counter++ ] = it.Get();
++it;
}
}
transformHigh->SetParameters( parametersHigh );
// Software Guide : BeginLatex
//
// We now pass the parameters of the high resolution transform as the initial
// parameters to be used in a second stage of the registration process.
//
// Software Guide : EndLatex
std::cout << "Starting Registration with high resolution transform" << std::endl;
// Software Guide : BeginCodeSnippet
registration->SetInitialTransformParameters(transformHigh->GetParameters());
registration->SetTransform( transformHigh );
// Software Guide : BeginLatex
//
// Typically, we will also want to tighten the optimizer parameters
// when we move from lower to higher resolution grid.
//
// Software Guide : EndLatex
optimizer->SetGradientConvergenceTolerance( 0.01 );
optimizer->SetDefaultStepLength( 0.25 );
try
{
registration->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
// Software Guide : EndCodeSnippet
// Finally we use the last transform parameters in order to resample the image.
//
transformHigh->SetParameters( registration->GetLastTransformParameters() );
using ResampleFilterType = itk::ResampleImageFilter<
MovingImageType,
FixedImageType >;
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform( transformHigh );
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 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 = transformHigh->TransformPoint( fixedPoint );
displacement = movingPoint - fixedPoint;
fi.Set( displacement );
++fi;
}
FieldWriterType::Pointer fieldWriter = FieldWriterType::New();
fieldWriter->SetInput( field );
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;
}