void Preprocess::GradientAnisotropicDiffusion( double timestep, double conductance, int iterations )
{
int size3 = myImg->GetLargestPossibleRegion().GetSize()[2];
if(size3 == 1)
{
typedef itk::GradientAnisotropicDiffusionImageFilter< ImageType2D, FloatImageType2D > FilterType;
FilterType::Pointer filter = FilterType::New();
filter->SetInput( this->ExtractSlice( myImg, 0) );
filter->SetTimeStep(timestep);
filter->SetConductanceParameter(conductance);
filter->SetNumberOfIterations(iterations);
try
{
filter->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ITK FILTER ERROR: " << err << std::endl ;
return;
}
ImageType2D::Pointer temp = RescaleFloatToImageType( filter->GetOutput() );
myImg = SliceTo3D( temp );
}
else
{
typedef itk::GradientAnisotropicDiffusionImageFilter< ImageType3D, FloatImageType3D > FilterType;
FilterType::Pointer filter = FilterType::New();
filter->SetInput(myImg);
filter->SetTimeStep(timestep);
filter->SetConductanceParameter(conductance);
filter->SetNumberOfIterations(iterations);
try
{
filter->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ITK FILTER ERROR: " << err << std::endl ;
return;
}
myImg = RescaleFloatToImageType( filter->GetOutput() );
}
}
void Preprocess::CannyEdgeDetection(float variance, float upperThreshold, float lowerThreshold)
{
typedef itk::CastImageFilter< ImageType3D, FloatImageType3D > CastFilterType;
CastFilterType::Pointer cast = CastFilterType::New();
cast->SetInput( myImg );
typedef itk::CannyEdgeDetectionImageFilter< FloatImageType3D, FloatImageType3D > FilterType;
FilterType::Pointer filter = FilterType::New();
filter->SetInput( cast->GetOutput() );
filter->SetUpperThreshold(upperThreshold); //Threshold for detected edges = threshold
filter->SetLowerThreshold(lowerThreshold); //Threshold for detected edges = threshold/2
//filter->SetThreshold(threshold); //Lowest allowed value in the output image
filter->SetVariance(variance); //For Gaussian smoothing
//filter->SetMaximumError(.01f); //For Gaussian smoothing
try
{
filter->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "Exception caught: " << err << std::endl;
}
myImg = RescaleFloatToImageType( filter->GetOutput() );
}
void Preprocess::DiscreteGaussianFilter(float varX, float varY, float varZ, float maxError)
{
typedef itk::DiscreteGaussianImageFilter<ImageType3D, FloatImageType3D> FilterType;
FilterType::Pointer filter = FilterType::New();
FilterType::ArrayType maxErr;
maxErr.Fill(maxError);
filter->SetMaximumError( maxErr );
FilterType::ArrayType variance;
variance[0] = varX;
variance[1] = varY;
variance[2] = varZ;
filter->SetVariance(variance);
filter->SetInput( myImg );
try
{
filter->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ITK FILTER ERROR: " << err << std::endl ;
return;
}
myImg = RescaleFloatToImageType( filter->GetOutput() );
}
bool VolumeProcess::RunGaussianSmoothing(float varX, float varY, float varZ, float maxErr)
{ //by xiao liang
typedef itk::DiscreteGaussianImageFilter< ImageType, FloatImageType3D > GaussianFilterType;
GaussianFilterType::Pointer GaussianFilter = GaussianFilterType::New();
GaussianFilter->SetInput(m_outputImage);
//GaussianFilter->SetFilterDimensionality(3);
GaussianFilterType::ArrayType maxErrTypeValue;
maxErrTypeValue.Fill(maxErr);
GaussianFilter->SetMaximumError( maxErrTypeValue );
GaussianFilterType::ArrayType variance;
variance[0] = varX;
variance[1] = varY;
variance[2] = varZ;
GaussianFilter->SetVariance(variance);
//GaussianFilter->SetMaximumKernelWidth(maxKernalWidth);
try
{
GaussianFilter->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ITK FILTER ERROR: " << err << std::endl ;
return false;
}
m_outputImage = RescaleFloatToImageType(GaussianFilter->GetOutput());
if(debug)
std::cerr << "GaussianFilter Filter Done" << std::endl;
return true;
}
void Preprocess::LaplacianOfGaussian(int sigma, int min)
{
int size3 = myImg->GetLargestPossibleRegion().GetSize()[2];
if(size3 == 1)
{
typedef itk::LaplacianRecursiveGaussianImageFilter< ImageType2D, FloatImageType2D > FilterType;
FilterType::Pointer laplacian = FilterType::New();
laplacian->SetNormalizeAcrossScale( true );
laplacian->SetSigma( sigma );
laplacian->SetInput( this->ExtractSlice( myImg, 0) );
try
{
laplacian->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ITK FILTER ERROR: " << err << std::endl ;
return;
}
ImageType2D::Pointer temp = RescaleFloatToImageType( laplacian->GetOutput(), min );
myImg = SliceTo3D( temp );
}
else
{
typedef itk::LaplacianRecursiveGaussianImageFilter< ImageType3D, FloatImageType3D > FilterType;
FilterType::Pointer laplacian = FilterType::New();
laplacian->SetNormalizeAcrossScale( true );
laplacian->SetSigma( sigma );
laplacian->SetInput( myImg );
try
{
laplacian->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ITK FILTER ERROR: " << err << std::endl ;
return;
}
myImg = RescaleFloatToImageType( laplacian->GetOutput(), min );
}
}
bool VolumeProcess:: RunIntensityNormalize()
{
//Rescale weights:
typedef itk::NormalizeImageFilter< ImageType, FloatImageType3D> NormalizeImageFilter;
NormalizeImageFilter::Pointer Normalize = NormalizeImageFilter::New();
Normalize->SetInput(m_outputImage);
Normalize->Update();
m_outputImage = RescaleFloatToImageType(Normalize->GetOutput());
return true;
}
void Preprocess::DanielssonDistanceMap(void)
{
typedef itk::DanielssonDistanceMapImageFilter<ImageType3D, FloatImageType3D> DT_Type;
DT_Type::Pointer filter = DT_Type::New();
filter->SetInput( myImg );
try
{
filter->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ITK FILTER ERROR: " << err << std::endl;
}
myImg = RescaleFloatToImageType( filter->GetDistanceMap() );
}
bool VolumeProcess:: RunDanielssonDistanceMap(void)
{
typedef itk::DanielssonDistanceMapImageFilter<ImageType, FloatImageType3D> DT_Type;
DT_Type::Pointer DTfilter = DT_Type::New();
DTfilter->SetInput( m_outputImage );
//DTfilter->GetDistanceMap();
try
{
DTfilter->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ITK FILTER ERROR: " << err << std::endl ;
return false;
}
m_outputImage = RescaleFloatToImageType(DTfilter->GetDistanceMap());
if(debug)
std::cerr << "DanielssonDistanceMap Filter Done" << std::endl;
return true;
}
