void meVTKSkeleton::ComputeAssociatedAverageOutwardFlux()
{
//To have a good quality gradient of the distance map, perform a light smooth over it. Define
//convolution kernels in each direction and use them recursively.
typedef itk::RecursiveGaussianImageFilter< SkeletonAPITypes::InputImageType , SkeletonAPITypes::InputImageType > RecursiveGaussianFilterType;
RecursiveGaussianFilterType::Pointer gaussianFilterX = RecursiveGaussianFilterType::New();
RecursiveGaussianFilterType::Pointer gaussianFilterY = RecursiveGaussianFilterType::New();
RecursiveGaussianFilterType::Pointer gaussianFilterZ = RecursiveGaussianFilterType::New();
gaussianFilterX->SetDirection( 0 );
gaussianFilterY->SetDirection( 1 );
gaussianFilterZ->SetDirection( 2 );
gaussianFilterX->SetOrder( RecursiveGaussianFilterType::ZeroOrder );
gaussianFilterY->SetOrder( RecursiveGaussianFilterType::ZeroOrder );
gaussianFilterZ->SetOrder( RecursiveGaussianFilterType::ZeroOrder );
gaussianFilterX->SetNormalizeAcrossScale( false );
gaussianFilterY->SetNormalizeAcrossScale( false );
gaussianFilterZ->SetNormalizeAcrossScale( false );
gaussianFilterX->SetInput( this->inputData );
gaussianFilterY->SetInput( gaussianFilterX->GetOutput() );
gaussianFilterZ->SetInput( gaussianFilterY->GetOutput() );
gaussianFilterX->SetSigma( this->sigma );
gaussianFilterY->SetSigma( this->sigma );
gaussianFilterZ->SetSigma( this->sigma );
typedef itk::GradientImageFilter< SkeletonAPITypes::InputImageType , SkeletonAPITypes::InputPixelType, SkeletonAPITypes::InputPixelType > GradientFilterType;
typedef GradientFilterType::OutputImageType::PixelType GradientPixelType;
// Compute the gradient.
GradientFilterType::Pointer gradientFilter = GradientFilterType::New();
gradientFilter->SetInput( gaussianFilterZ->GetOutput() );
// Compute the average outward flux.
typedef itk::AverageOutwardFluxImageFilter< SkeletonAPITypes::InputImageType, SkeletonAPITypes::InputPixelType, GradientPixelType > AOFFilterType;
AOFFilterType::Pointer aofFilter = AOFFilterType::New();
aofFilter->SetInput( this->inputData );
aofFilter->SetGradientImage( gradientFilter->GetOutput() );
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// 2. Compute the skeleton
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
typedef itk::MedialCurveImageFilter< SkeletonAPITypes::InputImageType > MedialCurveFilter;
MedialCurveFilter::Pointer medialFilter = MedialCurveFilter::New();
medialFilter->SetInput( this->inputData );
medialFilter->SetAverageOutwardFluxImage( aofFilter->GetOutput() );
medialFilter->SetThreshold( this->threshold );
medialFilter->Update();
this->outputData = medialFilter->GetOutput();
}
bool VolumeProcess::RunRecursiveGaussianIIRRilter(float sigmaX, float sigmaY, float sigmaZ)
{ //by xiao liang
typedef itk::RecursiveGaussianImageFilter< ImageType, ImageType > RecursiveGaussianFilterType;
RecursiveGaussianFilterType::Pointer RecursiveGaussianFilterX = RecursiveGaussianFilterType::New();
RecursiveGaussianFilterType::Pointer RecursiveGaussianFilterY = RecursiveGaussianFilterType::New();
RecursiveGaussianFilterType::Pointer RecursiveGaussianFilterZ = RecursiveGaussianFilterType::New();
RecursiveGaussianFilterX->SetDirection(0);
RecursiveGaussianFilterY->SetDirection(1);
RecursiveGaussianFilterZ->SetDirection(2);
RecursiveGaussianFilterX->SetOrder(RecursiveGaussianFilterType::ZeroOrder);
RecursiveGaussianFilterY->SetOrder(RecursiveGaussianFilterType::ZeroOrder);
RecursiveGaussianFilterZ->SetOrder(RecursiveGaussianFilterType::ZeroOrder);
RecursiveGaussianFilterX->SetNormalizeAcrossScale(false);
RecursiveGaussianFilterY->SetNormalizeAcrossScale(false);
RecursiveGaussianFilterZ->SetNormalizeAcrossScale(false);
RecursiveGaussianFilterX->SetSigma(sigmaX);
RecursiveGaussianFilterX->SetSigma(sigmaY);
RecursiveGaussianFilterX->SetSigma(sigmaZ);
RecursiveGaussianFilterX->SetInput(m_outputImage);
RecursiveGaussianFilterY->SetInput(RecursiveGaussianFilterX->GetOutput());
RecursiveGaussianFilterZ->SetInput(RecursiveGaussianFilterY->GetOutput());
try
{
RecursiveGaussianFilterZ->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ITK FILTER ERROR: " << err << std::endl ;
return false;
}
m_outputImage = RecursiveGaussianFilterZ->GetOutput();
if(debug)
std::cerr << " RecursiveGaussianFilter Done" << std::endl;
return true;
}
