[Insight-users] DeformableRegistration6 error
kingaza at gmail.com
kingaza at gmail.com
Wed Nov 3 00:56:17 EST 2004
I have read the example DeformableRegistration7.cxx, but still have no
idea about some parameters of LBFGSB optimizer.
when i run deformableRegistration6 using LBFGSBOptimizer, it seems
work well in low translation, but in high translation, it do nothing
but throw a exception. I think this is because of some worng
configurations to the optimizer, but i am not sure.
do you think I should read a paper for this optimizer? or is there any
tips to it?
thx again！
the attach is my modified code
these are the infomation in command window:
Starting Registration with low resolution transform
0 404.8 7.64756
1 241.448 5.85265
......
Starting Registration with high resolution transform
ExceptionObject caught !
itk::ExceptionObject (0109F7C4)
Location: "Unknown"
File: .\itkLBFGSBOptimizer.cxx
Line: 212
Descritption: itk::ERROR: LBFGSBOPtimizer(003B67F0): LowerBound array
does not have sufficient number element
On Tue, 02 Nov 2004 13:35:10 -0500, Luis Ibanez <luis.ibanez at kitware.com> wrote:
> Hi Kingaza,
>
> Please post to the list the error message that you
> get from the Exception.
>
> Did you follow the guidelines of the example
>
> DeformableRegistration7.cxx ??
>
> Please let us know,
>
> Thanks
>
>
>
>
> Luis
>
> ------------------------
> kingaza at gmail.com wrote:
>
> > thx for your help
> > and now I try to use LBGSBOptimizer inplace of LBGSOptimizer
> > but it seems that the two optimizers are different in use
> > for examle,
> > LBGSBOptimzer does not hava the member function :SetDefaultStepLength()
> >
> > and it seems some problems if I do in this way:
> >
> > //for transformLow
> > /* //for LBFGSOptimizer
> > optimizer->SetGradientConvergenceTolerance( 0.05 );
> > optimizer->SetLineSearchAccuracy( 0.9 );
> > optimizer->SetDefaultStepLength( 1.5 );
> > optimizer->TraceOn();
> > optimizer->SetMaximumNumberOfFunctionEvaluations( 1000 );
> > */
> > //for LBFGSOptimizer
> > OptimizerType::BoundSelectionType boundSelect(
> > transformLow->GetNumberOfParameters() );
> > OptimizerType::BoundValueType upperBound(
> > transformLow->GetNumberOfParameters() );
> > OptimizerType::BoundValueType lowerBound(
> > transformLow->GetNumberOfParameters() );
> >
> > boundSelect.Fill( 0 );
> > upperBound.Fill( 0.0 );
> > lowerBound.Fill( 0.0 );
> >
> > optimizer->SetBoundSelection( boundSelect );
> > optimizer->SetUpperBound( upperBound );
> > optimizer->SetLowerBound( lowerBound );
> >
> > optimizer->SetCostFunctionConvergenceFactor( 1e+12 );
> > optimizer->SetProjectedGradientTolerance( 1.0 );
> > optimizer->SetMaximumNumberOfIterations( 500 );
> > optimizer->SetMaximumNumberOfEvaluations( 500 );
> > optimizer->SetMaximumNumberOfCorrections( 5 );
> >
> > //for transformHigh
> > /* //for LBFGSOptimizer
> > optimizer->SetGradientConvergenceTolerance( 0.01 );
> > optimizer->SetDefaultStepLength( 0.25 );
> > */
> > //for LBFGSOptimizer
> > optimizer->SetProjectedGradientTolerance( 0.01 );
> >
> > but it can work only in the first registraion, and throw a exception
> > in the second registration.
> >
> > could you tell me how to use LBGSBOptimizer in a multi-resolution registration?
> >
> > On Sat, 30 Oct 2004 11:52:43 -0400, Luis Ibanez <luis.ibanez at kitware.com> wrote:
> >
> >>Hi Kingaza,
> >>
> >>This is a known problem of the LBGSOptimizer that tends to happen
> >>when at the first iteration the optimizer doesn't find a better point
> >>to move in the parametric space.
> >>
> >>It is unrelated to the fact that you are reading DICOM files and it is
> >>unrelated to the image size. The real cause is that you are inadvertedly
> >>starting the optimizer in a local optimium.
> >>
> >>Please try the new example
> >>
> >> DeformableRegistration7.cxx
> >>
> >>That uses the modified optimizer
> >>
> >> LBFGSBOptimizer
> >>
> >>This example was committed recently in CVS, so you will
> >>have to update your CVS checkout in order to get the file.
> >>
> >>http://www.itk.org/cgi-bin/viewcvs.cgi/Examples/Registration/DeformableRegistration7.cxx?rev=1.1&root=Insight&sortby=date&view=log
> >>
> >> Regards,
> >>
> >> Luis
> >>
> >>------------------------------------
> >>
> >>
> >>kingaza at gmail.com wrote:
> >>
> >>
> >>>hi all,
> >>>
> >>>I try DeformableRegistration6 using images which size is 256*256, but
> >>>an exception occures:
> >>>
> >>>itk::ExceptionObject (0108F840)
> >>>Location: "Unknown"
> >>>File: .\itkLBFGSOptimizer.cxx
> >>>LIne: 252
> >>>Description: itk::ERROR: LBFGSOpimeizer(00376680): Error occured in optimization
> >>>
> >>>and then i debug deeply, and find in itkLBFGSOptimizer.cxx
> >>>parameters.size = 0 while initialPosition.size() =128
> >>>this is where the exception is caught.
> >>>I had thought it is because of the dicom file format , so I try it
> >>>again using png file, and the same appears.
> >>>But if I use images which size is 64*64, anything is OK, and the
> >>>output is perfect.
> >>>please help me!
> >>>
> >>>
> >>>
> >>>itkLBFGSOptimizer.cxx
> >>>/////////////////////////////////////////////////////////////////////////////////////////////
> >>>......
> >>> m_VnlOptimizer->minimize( parameters );
> >>>
> >>>if ( parameters.size() != initialPosition.size() )
> >>> {
> >>> // set current position to initial position and throw an exception
> >>> this->SetCurrentPosition( initialPosition );
> >>> itkExceptionMacro( << "Error occured in optimization" );
> >>> }
> >>>......
> >>>/////////////////////////////////////////////////////////////////////////////////////////////
> >>>_______________________________________________
> >>>Insight-users mailing list
> >>>Insight-users at itk.org
> >>>http://www.itk.org/mailman/listinfo/insight-users
> >>>
> >>>
> >>>
> >>
> >>
> >
> >
>
>
-------------- next part --------------
/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: DeformableRegistration6.cxx,v $
Language: C++
Date: $Date: 2004/08/05 15:04:02 $
Version: $Revision: 1.3 $
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{BSplineDeformableTransform}
// class in a manually controlled multi-resolution scheme. Here we define two
// transforms at two different resolution levels. A first registration is
// perfomed 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{BSplineDeformableTransform} we omit here most of the details already
// discussed and will focus on the aspects related to the multi-resolution
// approach.
//
// \index{itk::BSplineDeformableTransform}
// \index{itk::BSplineDeformableTransform!DeformableRegistration}
// \index{itk::LBFGSOptimizer}
//
//
// Software Guide : EndLatex
#include "itkImageRegistrationMethod.h"
#include "itkMeanSquaresImageToImageMetric.h"
#include "itkLinearInterpolateImageFunction.h"
#include "itkImage.h"
// Software Guide : BeginLatex
//
// We include the header files for the transform and the optimizer.
//
// \index{itk::BSplineDeformableTransform!header}
// \index{itk::LBFGSOptimizer!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkBSplineDeformableTransform.h"
#include "itkLBFGSOptimizer.h"
#include "itkLBFGSBOptimizer.h"
// Software Guide : EndCodeSnippet
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkSquaredDifferenceImageFilter.h"
#include "itkBSplineResampleImageFunction.h"
#include "itkIdentityTransform.h"
#include "itkBSplineDecompositionImageFilter.h"
// NOTE: the LBFGSOptimizer does not invoke events
// 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:
typedef CommandIterationUpdate Self;
typedef itk::Command Superclass;
typedef itk::SmartPointer<Self> Pointer;
itkNewMacro( Self );
protected:
CommandIterationUpdate() {};
public:
typedef itk::LBFGSBOptimizer OptimizerType;
typedef const OptimizerType * OptimizerPointer;
void Execute(itk::Object *caller, const itk::EventObject & event)
{
Execute( (const itk::Object *)caller, event);
}
void Execute(const itk::Object * object, const itk::EventObject & event)
{
OptimizerPointer optimizer =
dynamic_cast< OptimizerPointer >( object );
if( typeid( event ) != typeid( itk::IterationEvent ) )
{
return;
}
std::cout << optimizer->GetCurrentIteration() << " ";
std::cout << optimizer->GetValue() << " ";
std::cout << optimizer->GetInfinityNormOfProjectedGradient() << 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 1;
}
const unsigned int ImageDimension = 2;
typedef float PixelType;
typedef itk::Image< PixelType, ImageDimension > FixedImageType;
typedef itk::Image< PixelType, ImageDimension > MovingImageType;
// Software Guide : BeginLatex
//
// We instantiate now the type of the \code{BSplineDeformableTransform} using
// as template parameters the type for coordinates representation, the
// dimension of the space, and the order of the BSpline.
//
// \index{BSplineDeformableTransform|New}
// \index{BSplineDeformableTransform|Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int SpaceDimension = ImageDimension;
const unsigned int SplineOrder = 3;
typedef double CoordinateRepType;
typedef itk::BSplineDeformableTransform<
CoordinateRepType,
SpaceDimension,
SplineOrder > TransformType;
// Software Guide : EndCodeSnippet
// typedef itk::LBFGSOptimizer OptimizerType;
typedef itk::LBFGSBOptimizer OptimizerType;
typedef itk::MeanSquaresImageToImageMetric<
FixedImageType,
MovingImageType > MetricType;
typedef itk:: LinearInterpolateImageFunction<
MovingImageType,
double > InterpolatorType;
typedef itk::ImageRegistrationMethod<
FixedImageType,
MovingImageType > RegistrationType;
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
typedef itk::ImageFileReader< FixedImageType > FixedImageReaderType;
typedef itk::ImageFileReader< MovingImageType > MovingImageReaderType;
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 );
// Software Guide : BeginLatex
//
// Here we define the parameters of the BSplineDeformableTransform grid. We
// arbitrarily decide to use a grid with $5 \times 5$ nodes within the image.
// The reader should note that the BSpline computation requires a
// finite support region ( 1 grid node at the lower borders and 2
// grid nodes at upper borders). Therefore in this example, we set
// the grid size to be $8 \times 8$ and place the grid origin such that
// grid node (1,1) coinicides with the first pixel in the fixed image.
//
// \index{BSplineDeformableTransform}
//
// Software Guide : EndLatex
typedef TransformType::RegionType RegionType;
RegionType bsplineRegionLow;
RegionType::SizeType gridBorderSize;
RegionType::SizeType totalGridSize;
gridBorderSize.Fill( 3 ); // Border for spline order = 3 ( 1 lower, 2 upper )
// Software Guide : BeginLatex
//
// Here we define the parameters of the BSpline transform at low resolution
//
// \index{BSplineDeformableTransform}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
RegionType::SizeType gridLowSizeOnImage;
gridLowSizeOnImage.Fill( 5 );
totalGridSize = gridLowSizeOnImage + gridBorderSize;
RegionType bsplineRegion;
bsplineRegion.SetSize( totalGridSize );
typedef TransformType::SpacingType SpacingType;
SpacingType spacingLow = fixedImage->GetSpacing();
typedef TransformType::OriginType OriginType;
OriginType originLow = fixedImage->GetOrigin();;
FixedImageType::SizeType fixedImageSize = fixedRegion.GetSize();
for(unsigned int r=0; r<ImageDimension; r++)
{
spacingLow[r] *= floor( static_cast<double>(fixedImageSize[r] - 1) /
static_cast<double>(gridLowSizeOnImage[r] - 1) );
originLow[r] -= spacingLow[r];
}
transformLow->SetGridSpacing( spacingLow );
transformLow->SetGridOrigin( originLow );
transformLow->SetGridRegion( bsplineRegion );
typedef TransformType::ParametersType 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 );
*/
OptimizerType::BoundSelectionType boundSelect( transformLow->GetNumberOfParameters() );
OptimizerType::BoundValueType upperBound( transformLow->GetNumberOfParameters() );
OptimizerType::BoundValueType lowerBound( transformLow->GetNumberOfParameters() );
boundSelect.Fill( 0 );
upperBound.Fill( 0.0 );
lowerBound.Fill( 0.0 );
optimizer->SetBoundSelection( boundSelect );
optimizer->SetUpperBound( upperBound );
optimizer->SetLowerBound( lowerBound );
optimizer->SetCostFunctionConvergenceFactor( 1e+12 );
optimizer->SetProjectedGradientTolerance( 1.0 );
optimizer->SetMaximumNumberOfIterations( 500 );
optimizer->SetMaximumNumberOfEvaluations( 500 );
optimizer->SetMaximumNumberOfCorrections( 5 );
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver( itk::IterationEvent(), observer );
std::cout << "Starting Registration with low resolution transform" << std::endl;
try
{
registration->StartRegistration();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return -1;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Once the registration has finished with the low resolution grid, we
// proceed to instantiate a higher resolution
// \code{BSplineDeformableTransform}.
//
// Software Guide : EndLatex
TransformType::Pointer transformHigh = TransformType::New();
RegionType::SizeType gridHighSizeOnImage;
gridHighSizeOnImage.Fill( 10 );
totalGridSize = gridHighSizeOnImage + gridBorderSize;
bsplineRegion.SetSize( totalGridSize );
SpacingType spacingHigh = fixedImage->GetSpacing();
OriginType originHigh = fixedImage->GetOrigin();;
for(unsigned int rh=0; rh<ImageDimension; rh++)
{
spacingHigh[rh] *= floor( static_cast<double>(fixedImageSize[rh] - 1) /
static_cast<double>(gridHighSizeOnImage[rh] - 1) );
originHigh[rh] -= spacingHigh[rh];
}
transformHigh->SetGridSpacing( spacingHigh );
transformHigh->SetGridOrigin( originHigh );
transformHigh->SetGridRegion( bsplineRegion );
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++ )
{
typedef TransformType::ImageType ParametersImageType;
typedef itk::ResampleImageFilter<ParametersImageType,ParametersImageType> ResamplerType;
ResamplerType::Pointer upsampler = ResamplerType::New();
typedef itk::BSplineResampleImageFunction<ParametersImageType,double> FunctionType;
FunctionType::Pointer function = FunctionType::New();
typedef itk::IdentityTransform<double,SpaceDimension> IdentityTransformType;
IdentityTransformType::Pointer identity = IdentityTransformType::New();
upsampler->SetInput( transformLow->GetCoefficientImage()[k] );
upsampler->SetInterpolator( function );
upsampler->SetTransform( identity );
upsampler->SetSize( transformHigh->GetGridRegion().GetSize() );
upsampler->SetOutputSpacing( transformHigh->GetGridSpacing() );
upsampler->SetOutputOrigin( transformHigh->GetGridOrigin() );
typedef itk::BSplineDecompositionImageFilter<ParametersImageType,ParametersImageType>
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
typedef itk::ImageRegionIterator<ParametersImageType> Iterator;
Iterator it( newCoefficients, transformHigh->GetGridRegion() );
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 );
*/
optimizer->SetProjectedGradientTolerance( 0.01 );
try
{
registration->StartRegistration();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return -1;
}
// Software Guide : EndCodeSnippet
// Finally we use the last transform parameters in order to resample the image.
//
transformHigh->SetParameters( registration->GetLastTransformParameters() );
typedef itk::ResampleImageFilter<
MovingImageType,
FixedImageType > ResampleFilterType;
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->SetDefaultPixelValue( 100 );
typedef unsigned char OutputPixelType;
typedef itk::Image< OutputPixelType, ImageDimension > OutputImageType;
typedef itk::CastImageFilter<
FixedImageType,
OutputImageType > CastFilterType;
typedef itk::ImageFileWriter< OutputImageType > WriterType;
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 -1;
}
typedef itk::SquaredDifferenceImageFilter<
FixedImageType,
FixedImageType,
OutputImageType > DifferenceFilterType;
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 -1;
}
}
// 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 -1;
}
}
// Generate the explicit deformation field resulting from
// the registration.
typedef itk::Vector< float, ImageDimension > VectorType;
typedef itk::Image< VectorType, ImageDimension > DeformationFieldType;
DeformationFieldType::Pointer field = DeformationFieldType::New();
field->SetRegions( fixedRegion );
field->SetOrigin( fixedImage->GetOrigin() );
field->SetSpacing( fixedImage->GetSpacing() );
field->Allocate();
typedef itk::ImageRegionIterator< DeformationFieldType > FieldIterator;
FieldIterator fi( field, fixedRegion );
fi.GoToBegin();
TransformType::InputPointType fixedPoint;
TransformType::OutputPointType movingPoint;
DeformationFieldType::IndexType index;
VectorType displacement;
while( ! fi.IsAtEnd() )
{
index = fi.GetIndex();
field->TransformIndexToPhysicalPoint( index, fixedPoint );
movingPoint = transformHigh->TransformPoint( fixedPoint );
displacement[0] = movingPoint[0] - fixedPoint[0];
displacement[1] = movingPoint[1] - fixedPoint[1];
fi.Set( displacement );
++fi;
}
typedef itk::ImageFileWriter< DeformationFieldType > FieldWriterType;
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 0;
}
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