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itkMRIBiasFieldCorrectionFilter.txx
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itkMRIBiasFieldCorrectionFilter.txx
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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkMRIBiasFieldCorrectionFilter.txx
Language: C++
Date: $Date$
Version: $Revision$
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.
=========================================================================*/
#ifndef __itkMRIBiasFieldCorrectionFilter_txx
#define __itkMRIBiasFieldCorrectionFilter_txx
#include "itkMRIBiasFieldCorrectionFilter.h"
namespace itk
{
// =========== MRIBiasEnergyFunction members =================
template<class TImage, class TImageMask, class TBiasField>
MRIBiasEnergyFunction<TImage, TImageMask, TBiasField>
::MRIBiasEnergyFunction()
{
m_BiasField = 0;
for (unsigned int i = 0; i < SpaceDimension; i++)
{
m_SamplingFactor[i] = 1;
}
m_Mask = NULL;
m_Image = NULL;
}
template<class TImage, class TImageMask, class TBiasField>
void
MRIBiasEnergyFunction<TImage, TImageMask, TBiasField>
::InitializeDistributions( Array<double> classMeans,
Array<double> classSigmas )
{
m_InternalEnergyFunction =
new InternalEnergyFunction(classMeans, classSigmas) ;
}
template<class TImage, class TImageMask, class TBiasField>
MRIBiasEnergyFunction<TImage, TImageMask, TBiasField>
::~MRIBiasEnergyFunction()
{
delete m_InternalEnergyFunction ;
m_InternalEnergyFunction = 0 ;
}
template<class TImage, class TImageMask, class TBiasField>
unsigned int
MRIBiasEnergyFunction<TImage, TImageMask, TBiasField>
::GetNumberOfParameters(void) const
{
if( m_BiasField == 0 )
{
return 0;
}
return m_BiasField->GetNumberOfCoefficients();
}
template<class TImage, class TImageMask, class TBiasField>
typename MRIBiasEnergyFunction<TImage, TImageMask, TBiasField>::MeasureType
MRIBiasEnergyFunction<TImage, TImageMask, TBiasField>
::GetValue( const ParametersType & parameters ) const
{
if ( m_Image.IsNull() )
{
itkExceptionMacro(<<"Image is null");
}
if( !m_InternalEnergyFunction )
{
itkExceptionMacro(<<"EnergyFunction is null");
}
if( m_BiasField == 0 )
{
itkExceptionMacro(<<"BiasField is null");
}
MeasureType total = 0.0;
m_BiasField->SetCoefficients(parameters) ;
// mask, bias and orginal image have the same dimensions
// and corresponding indexes
if ( m_SamplingFactor[0] == 1 &&
m_SamplingFactor[1] == 1 &&
m_SamplingFactor[2] == 1 )
{
ImageRegionIterator<ImageType> iIter(m_Image, m_Region) ;
typename TBiasField::SimpleForwardIterator bIter(m_BiasField) ;
bIter.Begin() ;
iIter.GoToBegin() ;
total = 0.0;
// fastest for full sampling
if (!m_Mask)
{
while (!bIter.IsAtEnd())
{
double diff = iIter.Get() - bIter.Get() ;
total = total + (*m_InternalEnergyFunction)(diff);
++bIter ;
++iIter ;
}
}
else
{
ImageRegionIterator<MaskType> mIter(m_Mask, m_Region) ;
mIter.GoToBegin();
while (!bIter.IsAtEnd())
{
if (mIter.Get() > 0.0)
{
double diff = iIter.Get() - bIter.Get() ;
total = total + (*m_InternalEnergyFunction)(diff);
}
++bIter ;
++iIter ;
++mIter;
}
}
}
else
{
typename ImageType::IndexType origIndex = m_Region.GetIndex();
typename ImageType::IndexType curIndex;
typename TBiasField::IndexType indexBias(SpaceDimension);
typename ImageType::SizeType size = m_Region.GetSize();
typedef typename ImageType::IndexType::IndexValueType ivtype;
// use indexing for incomplete sampling
if (!m_Mask)
{
indexBias[2] = 0;
for (curIndex[2] = origIndex[2];
curIndex[2] < origIndex[2] + (ivtype) size[2];
curIndex[2] = curIndex[2] + (ivtype) m_SamplingFactor[2] )
{
indexBias[1] = (int)((curIndex[2]%2) * 0.5 * m_SamplingFactor[1]);
for (curIndex[1] = origIndex[1];
curIndex[1] < origIndex[1] + (ivtype) size[1];
curIndex[1] = curIndex[1] + (ivtype) m_SamplingFactor[1])
{
indexBias[0] = (int)((curIndex[2]%2) * 0.5 * m_SamplingFactor[0]);
for (curIndex[0] = origIndex[0];
curIndex[0] < origIndex[0] + (ivtype) size[0];
curIndex[0] = curIndex[0] + (ivtype) m_SamplingFactor[0])
{
const double biasVal = m_BiasField->Evaluate(indexBias);
const double imageVal = m_Image->GetPixel(curIndex);
total += (*m_InternalEnergyFunction)(imageVal - biasVal);
indexBias[0] = indexBias[0] + m_SamplingFactor[0];
}
indexBias[1] = indexBias[1] + m_SamplingFactor[1];
}
indexBias[2] = indexBias[2] + m_SamplingFactor[2];
}
}
else
{
indexBias[2] = 0;
for (curIndex[2] = origIndex[2];
curIndex[2] < origIndex[2] + (ivtype) size[2];
curIndex[2] = curIndex[2] + (ivtype) m_SamplingFactor[2] )
{
indexBias[1] = (int)((curIndex[2]%2) * 0.5 * m_SamplingFactor[1]);
for (curIndex[1] = origIndex[1];
curIndex[1] < origIndex[1] + (ivtype) size[1];
curIndex[1] = curIndex[1] + (ivtype) m_SamplingFactor[1])
{
indexBias[0] = (int)((curIndex[2]%2) * 0.5 * m_SamplingFactor[0]);
for (curIndex[0] = origIndex[0];
curIndex[0] < origIndex[0] + (ivtype) size[0];
curIndex[0] = curIndex[0] + (ivtype) m_SamplingFactor[0])
{
if (m_Mask->GetPixel(curIndex) > 0.0)
{
const double biasVal = m_BiasField->Evaluate(indexBias);
const double imageVal = m_Image->GetPixel(curIndex);
total += (*m_InternalEnergyFunction)(imageVal - biasVal);
}
indexBias[0] = indexBias[0] + m_SamplingFactor[0];
}
indexBias[1] = indexBias[1] + m_SamplingFactor[1];
}
indexBias[2] = indexBias[2] + m_SamplingFactor[2];
}
}
}
//std::cout << "Value " << total << std::endl;
return total ;
}
// =========== MRIBiasFieldCorrectionFilter members ==================
template<class TInputImage, class TOutputImage, class TMaskImage>
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::MRIBiasFieldCorrectionFilter()
{
m_InputMask = 0 ;
m_OutputMask = 0 ;
m_BiasMultiplicative = true ;
m_BiasFieldDegree = 3 ;
m_OptimizerInitialRadius = 1.01 ;
m_VolumeCorrectionMaximumIteration = 2000 ;
m_InterSliceCorrectionMaximumIteration = 4000 ;
m_OptimizerGrowthFactor = 1.05 ;
m_OptimizerShrinkFactor = vcl_pow(m_OptimizerGrowthFactor, -0.25);
m_EnergyFunction = 0 ;
m_NormalVariateGenerator = NormalVariateGeneratorType::New() ;
//m_NormalVariateGenerator->Initialize(3024) ;
m_NormalVariateGenerator->Initialize(time(NULL)) ;
if (ImageDimension == 3)
{
m_UsingInterSliceIntensityCorrection = true ;
m_SlicingDirection = 2 ;
}
else
{
m_UsingInterSliceIntensityCorrection = false ;
}
m_UsingSlabIdentification = false ;
m_SlabBackgroundMinimumThreshold = NumericTraits< InputImagePixelType >::min() ;
m_SlabNumberOfSamples = 200 ;
m_SlabTolerance = 0.0 ;
m_UsingBiasFieldCorrection = true ;
m_GeneratingOutput = true ;
m_InternalInput = InternalImageType::New() ;
m_NumberOfLevels = 0;
this->SetNumberOfLevels( 2 );
}
template <class TInputImage, class TOutputImage, class TMaskImage>
void
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::SetNumberOfLevels( unsigned int num )
{
if( m_NumberOfLevels == num )
{
return;
}
this->Modified();
// clamp value to be at least one
m_NumberOfLevels = num;
if( m_NumberOfLevels < 1 )
{
m_NumberOfLevels = 1;
}
// resize the schedules
ScheduleType temp( m_NumberOfLevels, ImageDimension );
temp.Fill( 0 );
m_Schedule = temp;
// determine initial shrink factor
unsigned int startfactor = 1;
startfactor = startfactor << ( m_NumberOfLevels - 1 );
// set the starting shrink factors
this->SetStartingShrinkFactors( startfactor );
}
/*
* Set the starting shrink factors
*/
template <class TInputImage, class TOutputImage, class TMaskImage>
void
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::SetStartingShrinkFactors( unsigned int factor )
{
unsigned int array[ImageDimension];
for( unsigned int dim = 0; dim < ImageDimension; ++dim )
{
array[dim] = factor;
}
this->SetStartingShrinkFactors( array );
}
/*
* Set the starting shrink factors
*/
template <class TInputImage, class TOutputImage, class TMaskImage>
void
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::SetStartingShrinkFactors( unsigned int * factors )
{
for( unsigned int dim = 0; dim < ImageDimension; ++dim )
{
m_Schedule[0][dim] = factors[dim];
if( m_Schedule[0][dim] == 0 )
{
m_Schedule[0][dim] = 1;
}
}
for( unsigned int level = 1; level < m_NumberOfLevels; ++level )
{
for( unsigned int dim = 0; dim < ImageDimension; ++dim )
{
m_Schedule[level][dim] = m_Schedule[level-1][dim] / 2;
if( m_Schedule[level][dim] == 0 )
{
m_Schedule[level][dim] = 1;
}
}
}
this->Modified();
}
/*
* Get the starting shrink factors
*/
template <class TInputImage, class TOutputImage, class TMaskImage>
const unsigned int *
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::GetStartingShrinkFactors() const
{
return ( m_Schedule.data_block() );
}
/*
* Set the multi-resolution schedule
*/
template <class TInputImage, class TOutputImage, class TMaskImage>
void
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::SetSchedule( const ScheduleType& schedule )
{
if( schedule.rows() != m_NumberOfLevels ||
schedule.columns() != ImageDimension )
{
itkDebugMacro(<< "Schedule has wrong dimensions" );
return;
}
if( schedule == m_Schedule )
{
return;
}
this->Modified();
unsigned int level, dim;
for( level = 0; level < m_NumberOfLevels; level++ )
{
for( dim = 0; dim < ImageDimension; dim++ )
{
m_Schedule[level][dim] = schedule[level][dim];
// set schedule to max( 1, min(schedule[level],
// schedule[level-1] );
if( level > 0 )
{
m_Schedule[level][dim] = vnl_math_min( m_Schedule[level][dim],
m_Schedule[level-1][dim] );
}
if( m_Schedule[level][dim] < 1 )
{
m_Schedule[level][dim] = 1;
}
}
}
}
/*
* Is the schedule downward divisible ?
*/
template <class TInputImage, class TOutputImage, class TMaskImage>
bool
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::IsScheduleDownwardDivisible( const ScheduleType& schedule )
{
unsigned int ilevel, idim;
for( ilevel = 0; ilevel < schedule.rows() - 1; ilevel++ )
{
for( idim = 0; idim < schedule.columns(); idim++ )
{
if( schedule[ilevel][idim] == 0 )
{
return false;
}
if( ( schedule[ilevel][idim] % schedule[ilevel+1][idim] ) > 0 )
{
return false;
}
}
}
return true;
}
template<class TInputImage, class TOutputImage, class TMaskImage>
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::~MRIBiasFieldCorrectionFilter()
{
}
template<class TInputImage, class TOutputImage, class TMaskImage>
void
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::PrintSelf(std::ostream& os, Indent indent) const
{
Superclass::PrintSelf(os,indent);
os << indent << "Use interslice intensity correction: " ;
if ( m_UsingInterSliceIntensityCorrection )
{
os << "true" << std::endl ;
}
else
{
os << "false" << std::endl ;
}
os << indent << "Use slab identification: " ;
if ( m_UsingSlabIdentification )
{
os << "true" << std::endl ;
}
else
{
os << "false" << std::endl ;
}
os << indent << "Use biasfield correction: " ;
if ( m_UsingBiasFieldCorrection )
{
os << "true" << std::endl ;
}
else
{
os << "false" << std::endl ;
}
os << indent << "Generate output image: " ;
if ( m_GeneratingOutput )
{
os << "true" << std::endl ;
}
else
{
os << "false" << std::endl ;
}
os << indent << "Is bias field multiplicative: " ;
if ( m_BiasMultiplicative )
{
os << "true" << std::endl ;
}
else
{
os << "false" << std::endl ;
}
os << indent << "Biasfield degree = " << m_BiasFieldDegree << std::endl ;
os << indent << "Optimizer intial radius: " << m_OptimizerInitialRadius
<< std::endl ;
os << indent << "Optimizer growth factor: " << m_OptimizerGrowthFactor
<< std::endl ;
os << indent << "Optimizer shrink factor: " << m_OptimizerShrinkFactor
<< std::endl ;
os << indent << "Volume optimizer max iteration: "
<< m_VolumeCorrectionMaximumIteration << std::endl ;
os << indent << "Interslice correction optimizer max iteration: "
<< m_InterSliceCorrectionMaximumIteration << std::endl ;
os << indent << "Slicing direction: " << m_SlicingDirection << std::endl ;
os << indent << "InputMask: " ;
if ( m_InputMask.IsNotNull() )
{
os << m_InputMask << std::endl ;
}
else
{
os << "not set." << std::endl ;
}
os << indent << "OutputMask: " ;
if ( m_OutputMask.IsNotNull() )
{
os << m_OutputMask << std::endl ;
}
else
{
os << "not set." << std::endl ;
}
os << indent << "Energy function: " << m_EnergyFunction << std::endl ;
os << indent << "Normal random variate generator: "
<< m_NormalVariateGenerator << std::endl ;
os << indent << "Multires: No. levels: " << m_NumberOfLevels << std::endl;
os << indent << "Multires: Schedule: " << std::endl;
os << m_Schedule << std::endl;
}
template<class TInputImage, class TOutputImage, class TMaskImage>
void
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::SetInputMask(ImageMaskType* inputMask)
{
if ( m_InputMask != inputMask )
{
if (this->CheckMaskImage(inputMask))
{
m_InputMask = inputMask ;
this->Modified() ;
}
else
{
itkExceptionMacro("The size of the mask differs from the input image") ;
}
}
}
template<class TInputImage, class TOutputImage, class TMaskImage>
void
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::SetOutputMask(ImageMaskType* outputMask)
{
if ( m_OutputMask != outputMask )
{
if (this->CheckMaskImage(outputMask))
{
m_OutputMask = outputMask ;
this->Modified() ;
}
else
{
itkExceptionMacro("The size of the mask differs from the input image") ;
}
}
}
template<class TInputImage, class TOutputImage, class TMaskImage>
void
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::Initialize() throw (ExceptionObject)
{
m_InternalInput->SetRegions(this->GetInput()->GetLargestPossibleRegion()) ;
m_InternalInput->Allocate() ;
// copy the input image to the internal image and cast the type to
// float (InternalImageType::PixelType)
CopyAndConvertImage (this->GetInput(), m_InternalInput.GetPointer(),
this->GetInput()->GetLargestPossibleRegion()) ;
// if the bias is multiplicative, we will use logarithm
// the following section applies logarithm to key parameters and
// image data
if (this->IsBiasFieldMultiplicative())
{
const unsigned int size = m_TissueClassMeans.Size();
for( unsigned int i = 0 ; i < size; i++ )
{
m_TissueClassSigmas[i] = vcl_log(1.0 + m_TissueClassSigmas[i] /
(m_TissueClassMeans[i] + 1.0)) ;
m_TissueClassMeans[i] = vcl_log(m_TissueClassMeans[i] + 1.0) ;
}
m_OptimizerInitialRadius = vcl_log(1.0 + m_OptimizerInitialRadius) ;
this->Log1PImage(m_InternalInput.GetPointer(),
m_InternalInput.GetPointer()) ;
}
// initialize the energy function
if (m_TissueClassMeans.Size() < 1)
{
itkExceptionMacro(<<"Tissue Class Means is empty");
}
if (!m_EnergyFunction)
{
m_EnergyFunction = EnergyFunctionType::New();
m_EnergyFunction->InitializeDistributions(m_TissueClassMeans,
m_TissueClassSigmas) ;
}
m_EnergyFunction->SetImage(m_InternalInput.GetPointer()) ;
if ( m_InputMask.IsNotNull() )
{
m_EnergyFunction->SetMask(m_InputMask.GetPointer()) ;
}
}
template<class TInputImage, class TOutputImage, class TMaskImage>
typename MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>::BiasFieldType
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::EstimateBiasField(InputImageRegionType region,
unsigned int degree,
int maximumIteration)
{
itkDebugMacro(<< "Estimating bias field ");
bool cleanCoeffs = false;
BiasFieldType::DomainSizeType biasSize ;
this->GetBiasFieldSize(region, biasSize) ;
BiasFieldType bias(biasSize.size(), degree , biasSize) ;
/*
std::cout << "New bias field being computed = "
<< biasSize[0] << ", " << biasSize[1];
if(biasSize.size() > 2)
{
std::cout << ", " << biasSize[2];
}
std::cout << std::endl;
*/
if (bias.GetNumberOfCoefficients() == m_BiasFieldCoefficients.size())
{
bias.SetCoefficients(m_BiasFieldCoefficients) ;
}
else
{ // init ot all zero
cleanCoeffs = true;
m_BiasFieldCoefficients.clear();
for ( unsigned int i = 0 ; i < bias.GetNumberOfCoefficients() ; ++i )
{
m_BiasFieldCoefficients.push_back(0.0) ;
}
bias.SetCoefficients(m_BiasFieldCoefficients) ;
}
// update the energy function ;
m_EnergyFunction->SetBiasField(&bias) ;
m_EnergyFunction->SetRegion(region) ;
// initialize the 1+1 optimizer
OptimizerType::Pointer optimizer = OptimizerType::New() ;
optimizer->SetDebug(this->GetDebug()) ;
optimizer->SetNormalVariateGenerator(m_NormalVariateGenerator.GetPointer()) ;
optimizer->SetCostFunction(m_EnergyFunction.GetPointer()) ;
optimizer->SetMaximumIteration(maximumIteration) ;
if (m_OptimizerGrowthFactor > 0)
{
if (m_OptimizerShrinkFactor > 0)
{
optimizer->Initialize(m_OptimizerInitialRadius,
m_OptimizerGrowthFactor,
m_OptimizerShrinkFactor) ;
}
else
{
optimizer->Initialize(m_OptimizerInitialRadius,
m_OptimizerGrowthFactor) ;
}
}
else
{
if (m_OptimizerShrinkFactor > 0)
{
optimizer->Initialize(m_OptimizerInitialRadius,
m_OptimizerShrinkFactor) ;
}
else
{
optimizer->Initialize(m_OptimizerInitialRadius) ;
}
}
Array< double > scales(bias.GetNumberOfCoefficients()) ;
scales.Fill(100) ;
optimizer->SetScales(scales) ;
int noOfBiasFieldCoefficients = bias.GetNumberOfCoefficients() ;
typename EnergyFunctionType::ParametersType
initialPosition( noOfBiasFieldCoefficients );
for( int i=0; i < noOfBiasFieldCoefficients; i++ )
{
initialPosition[i] = bias.GetCoefficients()[i];
}
optimizer->SetInitialPosition( initialPosition );
//std::cout << " Initial Position = " << initialPosition << std::endl;
try
{
unsigned int level;
unsigned int dim;
for( level = 0; level < m_NumberOfLevels; level++ )
{
typename EnergyFunctionType::SamplingFactorType energySampling;
for( dim = 0; dim < ImageDimension; dim++ )
{
energySampling[dim] = m_Schedule[level][dim];
}
m_EnergyFunction->SetSamplingFactors(energySampling) ;
optimizer->MaximizeOff();
optimizer->StartOptimization();
/*
std::cout << " Level Results = " << optimizer->GetCurrentPosition()
<< std::endl;
*/
}
}
catch (ExceptionObject& ie)
{
std::cerr << ie << std::endl ;
}
catch (std::exception& e)
{
std::cerr << e.what() << std::endl ;
}
//std::cout << " Results = " << optimizer->GetCurrentPosition() <<std::endl;
bias.SetCoefficients(optimizer->GetCurrentPosition());
if (cleanCoeffs)
{
m_BiasFieldCoefficients.clear();
}
return bias ;
}
template<class TInputImage, class TOutputImage, class TMaskImage>
void
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::CorrectImage(BiasFieldType& bias,
InputImageRegionType region)
{
itkDebugMacro(<< "Correcting the image ");
typedef InternalImagePixelType Pixel ;
ImageRegionIterator<InternalImageType> iIter(m_InternalInput.GetPointer(),
region) ;
BiasFieldType::SimpleForwardIterator bIter(&bias) ;
BiasFieldType::DomainSizeType biasSize ;
this->GetBiasFieldSize(region, biasSize) ;
bIter.Begin() ;
iIter.GoToBegin() ;
if ( m_OutputMask.IsNotNull() )
{
itkDebugMacro(<< "Output mask is being used") ;
ImageRegionIterator<ImageMaskType> mIter(m_OutputMask, region) ;
mIter.GoToBegin() ;
while (!bIter.IsAtEnd())
{
double inputPixel = iIter.Get() ;
double diff = inputPixel - bIter.Get() ;
if (mIter.Get() > 0.0)
{
iIter.Set( (Pixel) diff) ;
}
else
{
iIter.Set( (Pixel) inputPixel) ;
}
++mIter ;
++bIter ;
++iIter ;
}
}
else
{
itkDebugMacro(<< "Output mask is not being used") ;
while (!bIter.IsAtEnd())
{
double diff = iIter.Get() - bIter.Get() ;
iIter.Set( (Pixel) diff) ;
++bIter ;
++iIter ;
}
}
}
template<class TInputImage, class TOutputImage, class TMaskImage>
void
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::CorrectInterSliceIntensityInhomogeneity(InputImageRegionType region)
{
long lastSlice = region.GetIndex()[m_SlicingDirection] +
static_cast<long>(region.GetSize()[m_SlicingDirection]) ;
InputImageRegionType sliceRegion ;
InputImageIndexType index = region.GetIndex() ;
InputImageSizeType size = region.GetSize() ;
sliceRegion.SetSize(size) ;
BiasFieldType bias = this->EstimateBiasField(sliceRegion, 0,
m_InterSliceCorrectionMaximumIteration);
double globalBiasCoef = bias.GetCoefficients()[0];
size[m_SlicingDirection] = 1 ;
sliceRegion.SetSize(size) ;
while (index[m_SlicingDirection] < lastSlice)
{
sliceRegion.SetIndex(index) ;
m_BiasFieldCoefficients.clear();
m_BiasFieldCoefficients.push_back( globalBiasCoef ) ;
bias = this->EstimateBiasField(sliceRegion, 0,
m_InterSliceCorrectionMaximumIteration);
this->CorrectImage(bias, sliceRegion) ;
index[m_SlicingDirection] += 1 ;
}
}
template<class TInputImage, class TOutputImage, class TMaskImage>
void
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::GenerateData()
{
itkDebugMacro(<< "Initializing filter...");
this->Initialize() ;
itkDebugMacro(<< "Filter initialized." );
if (m_UsingSlabIdentification)
{
itkDebugMacro(<< "Searching slabs...");
typename MRASlabIdentifier<InputImageType>::Pointer identifier =
MRASlabIdentifier<InputImageType>::New() ;
// find slabs
identifier->SetImage(this->GetInput()) ;
identifier->SetNumberOfSamples( m_SlabNumberOfSamples ) ;
identifier->SetBackgroundMinimumThreshold(
m_SlabBackgroundMinimumThreshold ) ;
identifier->SetTolerance( m_SlabTolerance ) ;
identifier->GenerateSlabRegions() ;
m_Slabs = identifier->GetSlabRegionVector() ;
itkDebugMacro(<< m_Slabs.size() << " slabs found.");
}
else
{
// creates a single region which is the largest possible region of
// the input image.
m_Slabs.clear();
m_Slabs.push_back(this->GetInput()->GetLargestPossibleRegion()) ;
}
this->AdjustSlabRegions(m_Slabs, this->GetOutput()->GetRequestedRegion()) ;
itkDebugMacro(<< "After adjustment, there are "
<< static_cast<unsigned long>( m_Slabs.size() )
<< " slabs.");
SlabRegionVectorIteratorType iter = m_Slabs.begin();
BiasFieldType::DomainSizeType biasSize ;
this->GetBiasFieldSize(*iter, biasSize) ;
BiasFieldType bias(biasSize.size(), m_BiasFieldDegree , biasSize) ;
int nCoef = bias.GetNumberOfCoefficients();
std::vector<double> lastBiasCoef;
lastBiasCoef.resize(nCoef);
for(int i=0; i<nCoef; i++)
{
lastBiasCoef[i] = 0;
}
while (iter != m_Slabs.end())
{
// correct inter-slice intensity inhomogeniety
// using 0th degree Legendre polynomial
if (m_UsingInterSliceIntensityCorrection)
{
itkDebugMacro(<< " Correcting inter-slice intensity..." );
this->CorrectInterSliceIntensityInhomogeneity(*iter) ;
itkDebugMacro(<< " Inter-slice intensity corrected.");
}
// correct 3D bias
if (m_UsingBiasFieldCorrection)
{
itkDebugMacro(<< " Correcting bias..." );
m_BiasFieldCoefficients.clear();
for(int i=0; i<nCoef; i++)
{
m_BiasFieldCoefficients.push_back( lastBiasCoef[i] ) ;
}
bias =
this->EstimateBiasField(*iter, m_BiasFieldDegree,
m_VolumeCorrectionMaximumIteration) ;
m_EstimatedBiasFieldCoefficients.resize( bias.GetCoefficients().size() ) ;
for (unsigned int k = 0 ; k < bias.GetCoefficients().size() ; k++)
{
m_EstimatedBiasFieldCoefficients[k] = bias.GetCoefficients()[k] ;
}
m_BiasFieldCoefficients.clear();
for(int i=0; i<nCoef; i++)
{
lastBiasCoef[i] = (bias.GetCoefficients()[i] + lastBiasCoef[i]) / 2;
m_BiasFieldCoefficients.push_back( lastBiasCoef[i] ) ;
}
this->CorrectImage(bias, *iter) ;
itkDebugMacro(<< " Bias corrected." );
}
iter++ ;
}
if (this->IsBiasFieldMultiplicative())
{
// for multiple calls it is necessary to restore the tissue classes
// and the Initial Radius (i.e. everything that has been
// log-transformed in the initialization)
const unsigned int size = m_TissueClassMeans.Size();
for( unsigned int i = 0 ; i < size; i++ )
{
m_TissueClassMeans[i] = vcl_exp(m_TissueClassMeans[i]) - 1.0 ;
m_TissueClassSigmas[i] = vcl_exp(m_TissueClassSigmas[i]) *
(1.0 + m_TissueClassMeans[i])
- m_TissueClassMeans[i];
}
m_OptimizerInitialRadius = vcl_exp(m_OptimizerInitialRadius) - 1.0 ;
}
if (m_GeneratingOutput)
{
itkDebugMacro( << "Generating the output image...");
if (this->IsBiasFieldMultiplicative())
{
ExpImage(m_InternalInput.GetPointer(), m_InternalInput.GetPointer()) ;
}
OutputImageType* output = this->GetOutput() ;
output->SetRegions(this->GetInput()->GetLargestPossibleRegion()) ;
output->Allocate() ;
CopyAndConvertImage(m_InternalInput.GetPointer(),
output, output->GetRequestedRegion()) ;
itkDebugMacro( << "The output image generated." );
}
}
// energy function related members
template<class TInputImage, class TOutputImage, class TMaskImage>
void
MRIBiasFieldCorrectionFilter<TInputImage, TOutputImage, TMaskImage>
::SetTissueClassStatistics( const Array<double> & means,
const Array<double> & sigmas)
throw (ExceptionObject)
{
size_t meanSize = means.Size() ;
size_t sigmaSize = sigmas.Size() ;