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/*=========================================================================
Program: Advanced Normalization Tools
Module: $RSfile: antsAffineInitializer.cxx,v $
Language: C++
Date: $Date: 2009/06/02 21:51:08 $
Version: $Revision: 1.103 $
Copyright (c) ConsortiumOfANTS. All rights reserved.
See accompanying COPYING.txt or
http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt 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.
=========================================================================*/
#include "antsUtilities.h"
#include <algorithm>
#include <vnl/vnl_inverse.h>
#include "antsAllocImage.h"
#include "itkImageMaskSpatialObject.h"
#include "itkANTSNeighborhoodCorrelationImageToImageMetricv4.h"
#include "itkArray.h"
#include "itkGradientImageFilter.h"
#include "itkBSplineControlPointImageFilter.h"
#include "itkBayesianClassifierImageFilter.h"
#include "itkBayesianClassifierInitializationImageFilter.h"
#include "itkBilateralImageFilter.h"
#include "itkCSVNumericObjectFileWriter.h"
#include "itkCastImageFilter.h"
#include "itkCompositeValleyFunction.h"
#include "itkConjugateGradientLineSearchOptimizerv4.h"
#include "itkConnectedComponentImageFilter.h"
#include "itkConstNeighborhoodIterator.h"
#include "itkCorrelationImageToImageMetricv4.h"
#include "itkDiscreteGaussianImageFilter.h"
#include "itkDistanceToCentroidMembershipFunction.h"
#include "itkDanielssonDistanceMapImageFilter.h"
#include "itkDemonsImageToImageMetricv4.h"
#include "itkExpImageFilter.h"
#include "itkExtractImageFilter.h"
#include "itkGaussianImageSource.h"
#include "itkGradientAnisotropicDiffusionImageFilter.h"
#include "itkGradientMagnitudeRecursiveGaussianImageFilter.h"
#include "itkHessianRecursiveGaussianImageFilter.h"
#include "itkHistogram.h"
#include "itkHistogramMatchingImageFilter.h"
#include "itkImage.h"
#include "itkImageClassifierBase.h"
#include "itkImageDuplicator.h"
#include "itkImageFileWriter.h"
#include "itkImageGaussianModelEstimator.h"
#include "itkImageKmeansModelEstimator.h"
#include "itkImageMomentsCalculator.h"
#include "itkImageRandomConstIteratorWithIndex.h"
#include "itkImageRegionIterator.h"
#include "itkImageRegionIteratorWithIndex.h"
#include "itkMattesMutualInformationImageToImageMetricv4.h"
#include "itkKdTree.h"
#include "itkKdTreeBasedKmeansEstimator.h"
#include "itkLabelContourImageFilter.h"
#include "itkLabelStatisticsImageFilter.h"
#include "itkLabeledPointSetFileReader.h"
#include "itkLabeledPointSetFileWriter.h"
#include "itkLaplacianRecursiveGaussianImageFilter.h"
#include "itkListSample.h"
#include "itkMRFImageFilter.h"
#include "itkMRIBiasFieldCorrectionFilter.h"
#include "itkMaskImageFilter.h"
#include "itkMaximumImageFilter.h"
#include "itkMedianImageFilter.h"
#include "itkMultiplyImageFilter.h"
#include "itkMultivariateLegendrePolynomial.h"
#include "itkMultiStartOptimizerv4.h"
#include "itkNeighborhood.h"
#include "itkNeighborhoodAlgorithm.h"
#include "itkNeighborhoodIterator.h"
#include "itkNormalVariateGenerator.h"
#include "itkOptimizerParameterScalesEstimator.h"
#include "itkOtsuThresholdImageFilter.h"
#include "itkRGBPixel.h"
#include "itkRegistrationParameterScalesFromPhysicalShift.h"
#include "itkRelabelComponentImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkSampleToHistogramFilter.h"
#include "itkScalarImageKmeansImageFilter.h"
#include "itkShrinkImageFilter.h"
#include "itkSimilarity3DTransform.h"
#include "itkSimilarity2DTransform.h"
#include "itkSize.h"
#include "itkSphereSpatialFunction.h"
#include "itkSTAPLEImageFilter.h"
#include "itkSubtractImageFilter.h"
#include "itkTDistribution.h"
#include "itkTimeProbe.h"
#include "itkTransformFileReader.h"
#include "itkTransformFileWriter.h"
#include "itkTranslationTransform.h"
#include "itkVariableSizeMatrix.h"
#include "itkVectorLinearInterpolateImageFunction.h"
#include "itkWeightedCentroidKdTreeGenerator.h"
#include "vnl/vnl_matrix_fixed.h"
#include "itkTransformFactory.h"
#include "itkSurfaceImageCurvature.h"
#include "itkMultiScaleLaplacianBlobDetectorImageFilter.h"
#include "itkEuler2DTransform.h"
#include "itkEuler3DTransform.h"
#include "itkCenteredAffineTransform.h"
#include "itkCompositeTransform.h"
#include <fstream>
#include <iostream>
#include <map> // Here I'm using a map but you could choose even other containers
#include <sstream>
#include <string>
#include "ReadWriteData.h"
#include "TensorFunctions.h"
#include "antsMatrixUtilities.h"
namespace ants
{
template <class TComputeType, unsigned int ImageDimension>
class SimilarityTransformTraits
{
// Don't worry about the fact that the default option is the
// affine Transform, that one will not actually be instantiated.
public:
typedef itk::AffineTransform<TComputeType, ImageDimension> TransformType;
};
template <>
class SimilarityTransformTraits<double, 2>
{
public:
typedef itk::Similarity2DTransform<double> TransformType;
};
template <>
class SimilarityTransformTraits<float, 2>
{
public:
typedef itk::Similarity2DTransform<float> TransformType;
};
template <>
class SimilarityTransformTraits<double, 3>
{
public:
typedef itk::Similarity3DTransform<double> TransformType;
};
template <>
class SimilarityTransformTraits<float, 3>
{
public:
typedef itk::Similarity3DTransform<float> TransformType;
};
template <unsigned int ImageDimension>
int antsAffineInitializerImp(int argc, char *argv[])
{
typedef double RealType;
typedef float PixelType;
/** Define All Parameters Here */
double pi = vnl_math::pi; // probably a vnl alternative
RealType searchfactor = 10; // in degrees, passed by user
unsigned int mibins = 32; // for mattes MI metric
RealType degtorad = 0.0174532925; // to convert degrees to radians
unsigned int localoptimizeriterations = 20; // for local search via conjgrad
// piover4 is (+/-) for cross-section of the sphere to multi-start search in increments
// of searchfactor ( converted from degrees to radians ).
// the search is centered +/- from the principal axis alignment of the images.
RealType piover4 = pi / 4; // works in preliminary practical examples in 3D, in 2D use pi.
bool useprincaxis = false;
typedef typename itk::ImageMaskSpatialObject<ImageDimension>::ImageType
maskimagetype;
std::string whichMetric=std::string("MI");
unsigned int localSearchIterations = 20;
typedef itk::TransformFileWriter TransformWriterType;
typedef itk::Vector<float, ImageDimension> VectorType;
typedef itk::Image<PixelType, ImageDimension> ImageType;
typedef typename itk::ImageMomentsCalculator<ImageType> ImageCalculatorType;
typedef itk::AffineTransform<RealType, ImageDimension> AffineType;
typedef typename SimilarityTransformTraits<RealType,
ImageDimension>::TransformType AffineTypeS;
typedef typename ImageCalculatorType::MatrixType MatrixType;
if( argc < 2 )
{
return 0;
}
int argct = 2;
std::string fn1 = std::string(argv[argct]); argct++;
std::string fn2 = std::string(argv[argct]); argct++;
std::string outname = std::string(argv[argct]); argct++;
if( argc > argct )
{
searchfactor = atof( argv[argct] ); argct++;
}
if( argc > argct )
{
RealType temp = atof( argv[argct] ); argct++;
if( temp > 1 )
{
temp = 1;
}
if( temp < 0.01 )
{
temp = 0.01;
}
piover4 = pi * temp;
}
if( argc > argct )
{
useprincaxis = atoi( argv[argct] ); argct++;
}
if( argc > argct )
{
localoptimizeriterations = atoi( argv[argct] ); argct++;
}
typename ImageType::Pointer image1 = ITK_NULLPTR;
typename ImageType::Pointer image2 = ITK_NULLPTR;
typename maskimagetype::Pointer mask = ITK_NULLPTR;
ReadImage<ImageType>(image1, fn1.c_str() );
ReadImage<ImageType>(image2, fn2.c_str() );
std::string maskfn = "";
if( argc > argct )
{
maskfn = std::string( argv[argct] ); argct++;
ReadImage<maskimagetype>(mask, maskfn.c_str() );
}
searchfactor *= degtorad; // convert degrees to radians
VectorType ccg1;
VectorType cpm1;
MatrixType cpa1;
VectorType ccg2;
VectorType cpm2;
MatrixType cpa2;
typename ImageCalculatorType::Pointer calculator1 = ImageCalculatorType::New();
typename ImageCalculatorType::Pointer calculator2 = ImageCalculatorType::New();
calculator1->SetImage( image1 );
calculator2->SetImage( image2 );
typename ImageCalculatorType::VectorType fixed_center;
fixed_center.Fill(0);
typename ImageCalculatorType::VectorType moving_center;
moving_center.Fill(0);
try
{
calculator1->Compute();
fixed_center = calculator1->GetCenterOfGravity();
ccg1 = calculator1->GetCenterOfGravity();
cpm1 = calculator1->GetPrincipalMoments();
cpa1 = calculator1->GetPrincipalAxes();
try
{
calculator2->Compute();
moving_center = calculator2->GetCenterOfGravity();
ccg2 = calculator2->GetCenterOfGravity();
cpm2 = calculator2->GetPrincipalMoments();
cpa2 = calculator2->GetPrincipalAxes();
}
catch( ... )
{
std::cerr << " zero image2 error ";
fixed_center.Fill(0);
}
}
catch( ... )
{
std::cerr << " zero image1 error ";
}
RealType bestscale =
calculator2->GetTotalMass() / calculator1->GetTotalMass();
RealType powlev = 1.0 / static_cast<RealType>(ImageDimension);
bestscale = vcl_pow( bestscale , powlev );
bestscale=1;
unsigned int eigind1 = 1;
unsigned int eigind2 = 1;
if( ImageDimension == 3 )
{
eigind1 = 2;
}
vnl_vector<RealType> evec1_2ndary = cpa1.GetVnlMatrix().get_row( eigind2 );
vnl_vector<RealType> evec1_primary = cpa1.GetVnlMatrix().get_row( eigind1 );
vnl_vector<RealType> evec2_2ndary = cpa2.GetVnlMatrix().get_row( eigind2 );
vnl_vector<RealType> evec2_primary = cpa2.GetVnlMatrix().get_row( eigind1 );
/** Solve Wahba's problem --- http://en.wikipedia.org/wiki/Wahba%27s_problem */
vnl_matrix<RealType> B = outer_product( evec2_primary, evec1_primary );
if( ImageDimension == 3 )
{
B = outer_product( evec2_2ndary, evec1_2ndary )
+ outer_product( evec2_primary, evec1_primary );
}
vnl_svd<RealType> wahba( B );
vnl_matrix<RealType> A_solution = wahba.V() * wahba.U().transpose();
A_solution = vnl_inverse( A_solution );
RealType det = vnl_determinant( A_solution );
if( det < 0 )
{
std::cerr << " bad det " << det << " v " << vnl_determinant( wahba.V() ) << " u "
<< vnl_determinant( wahba.U() ) << std::endl;
vnl_matrix<RealType> id( A_solution );
id.set_identity();
for( unsigned int i = 0; i < ImageDimension; i++ )
{
if( A_solution( i, i ) < 0 )
{
id( i, i ) = -1.0;
}
}
A_solution = A_solution * id.transpose();
std::cerr << " bad det " << det << " v " << vnl_determinant( wahba.V() ) << " u "
<< vnl_determinant( wahba.U() ) << " new " << vnl_determinant( A_solution ) << std::endl;
}
typename AffineType::Pointer affine1 = AffineType::New(); // translation to center
typename AffineType::OffsetType trans = affine1->GetOffset();
itk::Point<double, ImageDimension> trans2;
for( unsigned int i = 0; i < ImageDimension; i++ )
{
trans[i] = moving_center[i] - fixed_center[i];
trans2[i] = fixed_center[i] * ( 1 );
}
affine1->SetIdentity();
affine1->SetOffset( trans );
if( useprincaxis )
{
affine1->SetMatrix( A_solution );
}
affine1->SetCenter( trans2 );
{
typename TransformWriterType::Pointer transformWriter = TransformWriterType::New();
transformWriter->SetInput( affine1 );
transformWriter->SetFileName( outname.c_str() );
transformWriter->Update();
}
if( ImageDimension > 3 )
{
return EXIT_SUCCESS;
}
vnl_vector<RealType> evec_tert;
if( ImageDimension == 3 )
{ // try to rotate around tertiary and secondary axis
evec_tert = vnl_cross_3d( evec1_primary, evec1_2ndary );
}
if( ImageDimension == 2 )
{ // try to rotate around tertiary and secondary axis
evec_tert = evec1_2ndary;
evec1_2ndary = evec1_primary;
}
itk::Vector<RealType, ImageDimension> axis2;
itk::Vector<RealType, ImageDimension> axis1;
for( unsigned int d = 0; d < ImageDimension; d++ )
{
axis1[d] = evec_tert[d];
axis2[d] = evec1_2ndary[d];
}
typename AffineType::Pointer affinesearch = AffineType::New();
typedef itk::MultiStartOptimizerv4 OptimizerType;
typename OptimizerType::MetricValuesListType metricvalues;
typename OptimizerType::Pointer mstartOptimizer = OptimizerType::New();
typedef itk::CorrelationImageToImageMetricv4
<ImageType, ImageType, ImageType> GCMetricType;
typedef itk::MattesMutualInformationImageToImageMetricv4
<ImageType, ImageType, ImageType> MetricType;
typename MetricType::ParametersType newparams( affine1->GetParameters() );
typename GCMetricType::Pointer gcmetric = GCMetricType::New();
gcmetric->SetFixedImage( image1 );
gcmetric->SetVirtualDomainFromImage( image1 );
gcmetric->SetMovingImage( image2 );
gcmetric->SetMovingTransform( affinesearch );
gcmetric->SetParameters( newparams );
typename MetricType::Pointer mimetric = MetricType::New();
mimetric->SetNumberOfHistogramBins( mibins );
mimetric->SetFixedImage( image1 );
mimetric->SetMovingImage( image2 );
mimetric->SetMovingTransform( affinesearch );
mimetric->SetParameters( newparams );
if( mask.IsNotNull() )
{
typename itk::ImageMaskSpatialObject<ImageDimension>::Pointer so =
itk::ImageMaskSpatialObject<ImageDimension>::New();
so->SetImage( const_cast<maskimagetype *>( mask.GetPointer() ) );
mimetric->SetFixedImageMask( so );
gcmetric->SetFixedImageMask( so );
}
typedef itk::ConjugateGradientLineSearchOptimizerv4 LocalOptimizerType;
typename LocalOptimizerType::Pointer localoptimizer =
LocalOptimizerType::New();
RealType localoptimizerlearningrate = 0.1;
localoptimizer->SetLearningRate( localoptimizerlearningrate );
localoptimizer->SetMaximumStepSizeInPhysicalUnits(
localoptimizerlearningrate );
localoptimizer->SetNumberOfIterations( localSearchIterations );
localoptimizer->SetLowerLimit( 0 );
localoptimizer->SetUpperLimit( 2 );
localoptimizer->SetEpsilon( 0.1 );
localoptimizer->SetMaximumLineSearchIterations( 10 );
localoptimizer->SetDoEstimateLearningRateOnce( true );
localoptimizer->SetMinimumConvergenceValue( 1.e-6 );
localoptimizer->SetConvergenceWindowSize( 5 );
if( true )
{
typedef typename MetricType::FixedSampledPointSetType PointSetType;
typedef typename PointSetType::PointType PointType;
typename PointSetType::Pointer pset(PointSetType::New());
unsigned int ind=0;
unsigned int ct=0;
itk::ImageRegionIteratorWithIndex<ImageType> It(image1,
image1->GetLargestPossibleRegion() );
for( It.GoToBegin(); !It.IsAtEnd(); ++It )
{
// take every N^th point
if ( ct % 10 == 0 )
{
PointType pt;
image1->TransformIndexToPhysicalPoint( It.GetIndex(), pt);
pset->SetPoint(ind, pt);
ind++;
}
ct++;
}
mimetric->SetFixedSampledPointSet( pset );
mimetric->SetUseFixedSampledPointSet( true );
gcmetric->SetFixedSampledPointSet( pset );
gcmetric->SetUseFixedSampledPointSet( true );
}
if ( whichMetric.compare("MI") == 0 ) {
mimetric->Initialize();
typedef itk::RegistrationParameterScalesFromPhysicalShift<MetricType>
RegistrationParameterScalesFromPhysicalShiftType;
typename RegistrationParameterScalesFromPhysicalShiftType::Pointer
shiftScaleEstimator =
RegistrationParameterScalesFromPhysicalShiftType::New();
shiftScaleEstimator->SetMetric( mimetric );
shiftScaleEstimator->SetTransformForward( true );
typename RegistrationParameterScalesFromPhysicalShiftType::ScalesType
movingScales( affinesearch->GetNumberOfParameters() );
shiftScaleEstimator->EstimateScales( movingScales );
mstartOptimizer->SetScales( movingScales );
mstartOptimizer->SetMetric( mimetric );
localoptimizer->SetMetric( mimetric );
localoptimizer->SetScales( movingScales );
}
if ( whichMetric.compare("MI") != 0 ) {
gcmetric->Initialize();
typedef itk::RegistrationParameterScalesFromPhysicalShift<GCMetricType>
RegistrationParameterScalesFromPhysicalShiftType;
typename RegistrationParameterScalesFromPhysicalShiftType::Pointer
shiftScaleEstimator =
RegistrationParameterScalesFromPhysicalShiftType::New();
shiftScaleEstimator->SetMetric( gcmetric );
shiftScaleEstimator->SetTransformForward( true );
typename RegistrationParameterScalesFromPhysicalShiftType::ScalesType
movingScales( affinesearch->GetNumberOfParameters() );
shiftScaleEstimator->EstimateScales( movingScales );
mstartOptimizer->SetScales( movingScales );
mstartOptimizer->SetMetric( gcmetric );
localoptimizer->SetMetric( gcmetric );
localoptimizer->SetScales( movingScales );
}
typename OptimizerType::ParametersListType parametersList =
mstartOptimizer->GetParametersList();
for( double ang1 = ( piover4 * (-1) ); ang1 <= ( piover4 + searchfactor ); ang1 = ang1 + searchfactor )
{
if( useprincaxis )
{
affinesearch->SetMatrix( A_solution );
}
if( ImageDimension == 3 )
{
for( double ang2 = ( piover4 * (-1) );
ang2 <= ( piover4 + searchfactor );
ang2 = ang2 + searchfactor )
{
affinesearch->SetIdentity();
affinesearch->SetCenter( trans2 );
affinesearch->SetOffset( trans );
if( useprincaxis )
{
affinesearch->SetMatrix( A_solution );
}
affinesearch->Rotate3D(axis1, ang1, 1);
affinesearch->Rotate3D(axis2, ang2, 1);
affinesearch->Scale( bestscale );
parametersList.push_back( affinesearch->GetParameters() );
}
}
if( ImageDimension == 2 )
{
affinesearch->SetIdentity();
affinesearch->SetCenter( trans2 );
affinesearch->SetOffset( trans );
if( useprincaxis )
{
affinesearch->SetMatrix( A_solution );
}
affinesearch->Rotate2D( ang1, 1);
affinesearch->Scale( bestscale );
parametersList.push_back( affinesearch->GetParameters() );
}
}
mstartOptimizer->SetParametersList( parametersList );
if( localSearchIterations > 0 )
{
mstartOptimizer->SetLocalOptimizer( localoptimizer );
}
mstartOptimizer->StartOptimization();
typename AffineType::Pointer bestaffine = AffineType::New();
bestaffine->SetCenter( trans2 );
bestaffine->SetParameters( mstartOptimizer->GetBestParameters() );
typename TransformWriterType::Pointer transformWriter = TransformWriterType::New();
transformWriter->SetInput( bestaffine );
transformWriter->SetFileName( outname.c_str() );
transformWriter->Update();
return EXIT_SUCCESS;
}
// entry point for the library; parameter 'args' is equivalent to 'argv' in (argc,argv) of commandline parameters to
// 'main()'
int antsAffineInitializer( std::vector<std::string> args, std::ostream* /*out_stream = NULL */ )
{
// put the arguments coming in as 'args' into standard (argc,argv) format;
// 'args' doesn't have the command name as first, argument, so add it manually;
// 'args' may have adjacent arguments concatenated into one argument,
// which the parser should handle
args.insert( args.begin(), "antsAffineInitializer" );
int argc = args.size();
char* * argv = new char *[args.size() + 1];
for( unsigned int i = 0; i < args.size(); ++i )
{
// allocate space for the string plus a null character
argv[i] = new char[args[i].length() + 1];
std::strncpy( argv[i], args[i].c_str(), args[i].length() );
// place the null character in the end
argv[i][args[i].length()] = '\0';
}
argv[argc] = ITK_NULLPTR;
// class to automatically cleanup argv upon destruction
class Cleanup_argv
{
public:
Cleanup_argv( char* * argv_, int argc_plus_one_ ) : argv( argv_ ), argc_plus_one( argc_plus_one_ )
{
}
~Cleanup_argv()
{
for( unsigned int i = 0; i < argc_plus_one; ++i )
{
delete[] argv[i];
}
delete[] argv;
}
private:
char* * argv;
unsigned int argc_plus_one;
};
Cleanup_argv cleanup_argv( argv, argc + 1 );
// antscout->set_stream( out_stream );
if( argc < 3 )
{
std::cerr << "\nUsage: " << argv[0]
<<
" ImageDimension <Image1.ext> <Image2.ext> TransformOutput.mat Optional-SearchFactor Optional-Radian-Fraction Optional-bool-UsePrincipalAxes Optional-uint-UseLocalSearch Optional-Image1Mask "
<< std::endl;
std::cerr << " Optional-SearchFactor is in degrees --- e.g. 10 = search in 10 degree increments ." << std::endl;
std::cerr << " Radian-Fraction should be between 0 and 1 --- will search this arc +/- around principal axis."
<< std::endl;
std::cerr
<<
" Optional-bool-UsePrincipalAxes determines whether the rotation is searched around an initial principal axis alignment. Default = false. "
<< std::endl;
std::cerr
<<
" Optional-uint-UseLocalSearch determines if a local optimization is run at each search point for the set number of iterations. Default = 20."
<< std::endl;
return 0;
}
switch( atoi(argv[1]) )
{
case 2:
{
return antsAffineInitializerImp<2>(argc, argv);
}
case 3:
{
return antsAffineInitializerImp<3>(argc, argv);
}
case 4:
return antsAffineInitializerImp<4>(argc, argv);
}
return antsAffineInitializerImp<2>(argc, argv);
}
} // namespace ants
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