void QmitkPreprocessingView::DoAdcCalculation()
{
if (m_DiffusionImage.IsNull())
return;
typedef mitk::DiffusionImage< DiffusionPixelType > DiffusionImageType;
typedef itk::AdcImageFilter< DiffusionPixelType, double > FilterType;
for (unsigned int i=0; i<m_SelectedDiffusionNodes.size(); i++)
{
DiffusionImageType::Pointer inImage = dynamic_cast< DiffusionImageType* >(m_SelectedDiffusionNodes.at(i)->GetData());
FilterType::Pointer filter = FilterType::New();
filter->SetInput(inImage->GetVectorImage());
filter->SetGradientDirections(inImage->GetDirections());
filter->SetB_value(inImage->GetReferenceBValue());
filter->Update();
mitk::Image::Pointer image = mitk::Image::New();
image->InitializeByItk( filter->GetOutput() );
image->SetVolume( filter->GetOutput()->GetBufferPointer() );
mitk::DataNode::Pointer imageNode = mitk::DataNode::New();
imageNode->SetData( image );
QString name = m_SelectedDiffusionNodes.at(i)->GetName().c_str();
imageNode->SetName((name+"_ADC").toStdString().c_str());
GetDefaultDataStorage()->Add(imageNode);
}
}
void QmitkPreprocessingView::DoLengthCorrection()
{
if (m_DiffusionImage.IsNull())
return;
typedef mitk::DiffusionImage<DiffusionPixelType> DiffusionImageType;
typedef itk::DwiGradientLengthCorrectionFilter FilterType;
FilterType::Pointer filter = FilterType::New();
filter->SetRoundingValue( m_Controls->m_B_ValueMap_Rounder_SpinBox->value());
filter->SetReferenceBValue(m_DiffusionImage->GetReferenceBValue());
filter->SetReferenceGradientDirectionContainer(m_DiffusionImage->GetDirections());
filter->Update();
DiffusionImageType::Pointer image = DiffusionImageType::New();
image->SetVectorImage( m_DiffusionImage->GetVectorImage());
image->SetReferenceBValue( filter->GetNewBValue() );
image->SetDirections( filter->GetOutputGradientDirectionContainer());
image->InitializeFromVectorImage();
mitk::DataNode::Pointer imageNode = mitk::DataNode::New();
imageNode->SetData( image );
QString name = m_SelectedDiffusionNodes.front()->GetName().c_str();
imageNode->SetName((name+"_rounded").toStdString().c_str());
GetDefaultDataStorage()->Add(imageNode);
}
void QmitkDenoisingView::AfterThread()
{
m_ThreadIsRunning = false;
// stop timer to stop updates of progressbar
m_DenoisingTimer->stop();
// make sure progressbar is finished
mitk::ProgressBar::GetInstance()->Progress(m_MaxProgressCount);
if (m_CompletedCalculation)
{
switch (m_SelectedFilter)
{
case NOFILTERSELECTED:
case GAUSS:
{
break;
}
case NLM:
{
DiffusionImageType::Pointer image = DiffusionImageType::New();
image->SetVectorImage(m_NonLocalMeansFilter->GetOutput());
image->SetReferenceBValue(m_InputImage->GetReferenceBValue());
image->SetDirections(m_InputImage->GetDirections());
image->InitializeFromVectorImage();
mitk::DataNode::Pointer imageNode = mitk::DataNode::New();
imageNode->SetData( image );
QString name = m_ImageNode->GetName().c_str();
//TODO: Rician adaption & joint information in name
if (m_Controls->m_RicianCheckbox->isChecked() && !m_Controls->m_JointInformationCheckbox->isChecked())
{
imageNode->SetName((name+"_NLMr_"+QString::number(m_Controls->m_SpinBoxParameter1->value())+"-"+QString::number(m_Controls->m_SpinBoxParameter2->value())).toStdString().c_str());
}
else if(!m_Controls->m_RicianCheckbox->isChecked() && m_Controls->m_JointInformationCheckbox->isChecked())
{
imageNode->SetName((name+"_NLMv_"+QString::number(m_Controls->m_SpinBoxParameter1->value())+"-"+QString::number(m_Controls->m_SpinBoxParameter2->value())).toStdString().c_str());
}
else if(m_Controls->m_RicianCheckbox->isChecked() && m_Controls->m_JointInformationCheckbox->isChecked())
{
imageNode->SetName((name+"_NLMvr_"+QString::number(m_Controls->m_SpinBoxParameter1->value())+"-"+QString::number(m_Controls->m_SpinBoxParameter2->value())).toStdString().c_str());
}
else
{
imageNode->SetName((name+"_NLM_"+QString::number(m_Controls->m_SpinBoxParameter1->value())+"-"+QString::number(m_Controls->m_SpinBoxParameter2->value())).toStdString().c_str());
}
GetDefaultDataStorage()->Add(imageNode);
break;
}
}
}
m_Controls->m_ParameterBox->setEnabled(true);
m_Controls->m_ApplyButton->setText("Apply");
}
void QmitkRegistrationWorker::run()
{
typedef mitk::DiffusionImage<DiffusionPixelType> DiffusionImageType;
typedef DiffusionImageType::BValueMap BValueMap;
for( int i=0; i< m_View->m_SelectedDiffusionNodes.size(); i++)
{
m_View->m_GlobalRegisterer = QmitkDiffusionRegistrationView::DWIHeadMotionCorrectionFilterType::New();
mitk::DataNode::Pointer node = m_View->m_SelectedDiffusionNodes.at(i);
DiffusionImageType::Pointer inImage = dynamic_cast<mitk::DiffusionImage<short>*>(node->GetData());
if(inImage.IsNull())
{
MITK_ERROR << "Error occured: can't get input image. \nAborting";
return;
}
m_View->m_GlobalRegisterer->SetInput(inImage);
try{
m_View->m_GlobalRegisterer->Update();
}
catch( mitk::Exception e )
{
MITK_ERROR << "Internal error occured: " << e.what() << "\nAborting";
}
if( m_View->m_GlobalRegisterer->GetIsInValidState() )
{
DiffusionImageType::Pointer image = m_View->m_GlobalRegisterer->GetOutput();
mitk::DataNode::Pointer imageNode = mitk::DataNode::New();
imageNode->SetData( image );
QString name = node->GetName().c_str();
imageNode->SetName((name+"_MC").toStdString().c_str());
m_View->GetDataStorage()->Add(imageNode);
}
}
m_View->m_RegistrationThread.quit();
}
/**
* Denoises DWI using the Nonlocal - Means algorithm
*/
int DwiDenoising(int argc, char* argv[])
{
ctkCommandLineParser parser;
parser.setArgumentPrefix("--", "-");
parser.addArgument("input", "i", ctkCommandLineParser::String, "input image (DWI)", us::Any(), false);
parser.addArgument("variance", "v", ctkCommandLineParser::Float, "noise variance", us::Any(), false);
parser.addArgument("mask", "m", ctkCommandLineParser::String, "brainmask for input image", us::Any(), true);
parser.addArgument("search", "s", ctkCommandLineParser::Int, "search radius", us::Any(), true);
parser.addArgument("compare", "c", ctkCommandLineParser::Int, "compare radius", us::Any(), true);
parser.addArgument("joint", "j", ctkCommandLineParser::Bool, "use joint information");
parser.addArgument("rician", "r", ctkCommandLineParser::Bool, "use rician adaption");
map<string, us::Any> parsedArgs = parser.parseArguments(argc, argv);
if (parsedArgs.size()==0)
return EXIT_FAILURE;
string inFileName = us::any_cast<string>(parsedArgs["input"]);
double variance = static_cast<double>(us::any_cast<float>(parsedArgs["variance"]));
string maskName;
if (parsedArgs.count("mask"))
maskName = us::any_cast<string>(parsedArgs["mask"]);
string outFileName = inFileName;
boost::algorithm::erase_all(outFileName, ".dwi");
int search = 4;
if (parsedArgs.count("search"))
search = us::any_cast<int>(parsedArgs["search"]);
int compare = 1;
if (parsedArgs.count("compare"))
compare = us::any_cast<int>(parsedArgs["compare"]);
bool joint = false;
if (parsedArgs.count("joint"))
joint = true;
bool rician = false;
if (parsedArgs.count("rician"))
rician = true;
try
{
if( boost::algorithm::ends_with(inFileName, ".dwi"))
{
DiffusionImageType::Pointer dwi = dynamic_cast<DiffusionImageType*>(LoadFile(inFileName).GetPointer());
itk::NonLocalMeansDenoisingFilter<short>::Pointer filter = itk::NonLocalMeansDenoisingFilter<short>::New();
filter->SetNumberOfThreads(12);
filter->SetInputImage(dwi->GetVectorImage());
if (!maskName.empty())
{
mitk::Image::Pointer mask = dynamic_cast<mitk::Image*>(LoadFile(maskName).GetPointer());
ImageType::Pointer itkMask = ImageType::New();
mitk::CastToItkImage(mask, itkMask);
filter->SetInputMask(itkMask);
}
filter->SetUseJointInformation(joint);
filter->SetUseRicianAdaption(rician);
filter->SetSearchRadius(search);
filter->SetComparisonRadius(compare);
filter->SetVariance(variance);
filter->Update();
DiffusionImageType::Pointer output = DiffusionImageType::New();
output->SetVectorImage(filter->GetOutput());
output->SetReferenceBValue(dwi->GetReferenceBValue());
output->SetDirections(dwi->GetDirections());
output->InitializeFromVectorImage();
std::stringstream name;
name << outFileName << "_NLM_" << search << "-" << compare << "-" << variance << ".dwi";
MITK_INFO << "Writing: " << name.str();
mitk::NrrdDiffusionImageWriter<short>::Pointer writer = mitk::NrrdDiffusionImageWriter<short>::New();
writer->SetInput(output);
writer->SetFileName(name.str());
writer->Update();
MITK_INFO << "Finish!";
}
else
{
MITK_INFO << "Only supported for .dwi!";
}
}
catch (itk::ExceptionObject e)
{
MITK_INFO << e;
return EXIT_FAILURE;
}
catch (std::exception e)
{
MITK_INFO << e.what();
return EXIT_FAILURE;
}
catch (...)
{
MITK_INFO << "ERROR!?!";
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
void mitk::RegistrationWrapper::ApplyTransformationToImage(mitk::Image::Pointer img, const mitk::RegistrationWrapper::RidgidTransformType &transformation,double* offset, mitk::Image* resampleReference, bool binary)
{
typedef mitk::DiffusionImage<short> DiffusionImageType;
if (dynamic_cast<DiffusionImageType*> (img.GetPointer()) == NULL)
{
ItkImageType::Pointer itkImage = ItkImageType::New();
MITK_ERROR << "imgCopy 0 " << "/" << img->GetReferenceCount();
MITK_ERROR << "pixel type " << img->GetPixelType().GetComponentTypeAsString();
CastToItkImage(img, itkImage);
typedef itk::Euler3DTransform< double > RigidTransformType;
RigidTransformType::Pointer rtransform = RigidTransformType::New();
RigidTransformType::ParametersType parameters(RigidTransformType::ParametersDimension);
for (int i = 0; i<6;++i)
parameters[i] = transformation[i];
rtransform->SetParameters( parameters );
mitk::Point3D origin = itkImage->GetOrigin();
origin[0]-=offset[0];
origin[1]-=offset[1];
origin[2]-=offset[2];
mitk::Point3D newOrigin = rtransform->GetInverseTransform()->TransformPoint(origin);
itk::Matrix<double,3,3> dir = itkImage->GetDirection();
itk::Matrix<double,3,3> transM ( vnl_inverse(rtransform->GetMatrix().GetVnlMatrix()));
itk::Matrix<double,3,3> newDirection = transM * dir;
itkImage->SetOrigin(newOrigin);
itkImage->SetDirection(newDirection);
// Perform Resampling if reference image is provided
if (resampleReference != NULL)
{
typedef itk::ResampleImageFilter<ItkImageType, ItkImageType> ResampleFilterType;
ItkImageType::Pointer itkReference = ItkImageType::New();
CastToItkImage(resampleReference,itkReference);
typedef itk::WindowedSincInterpolateImageFunction< ItkImageType, 3> WindowedSincInterpolatorType;
WindowedSincInterpolatorType::Pointer sinc_interpolator = WindowedSincInterpolatorType::New();
typedef itk::NearestNeighborInterpolateImageFunction< ItkImageType, double > NearestNeighborInterpolatorType;
NearestNeighborInterpolatorType::Pointer nn_interpolator = NearestNeighborInterpolatorType::New();
ResampleFilterType::Pointer resampler = ResampleFilterType::New();
resampler->SetInput(itkImage);
resampler->SetReferenceImage( itkReference );
resampler->UseReferenceImageOn();
if (binary)
resampler->SetInterpolator(nn_interpolator);
else
resampler->SetInterpolator(sinc_interpolator);
resampler->Update();
GrabItkImageMemory(resampler->GetOutput(), img);
}
else
{
// !! CastToItk behaves very differently depending on the original data type
// if the target type is the same as the original, only a pointer to the data is set
// and an additional GrabItkImageMemory will cause a segfault when the image is destroyed
// GrabItkImageMemory - is not necessary in this case since we worked on the original data
// See Bug 17538.
if (img->GetPixelType().GetComponentTypeAsString() != "double")
img = GrabItkImageMemory(itkImage);
}
}
else
{
DiffusionImageType::Pointer diffImages = dynamic_cast<DiffusionImageType*>(img.GetPointer());
typedef itk::Euler3DTransform< double > RigidTransformType;
RigidTransformType::Pointer rtransform = RigidTransformType::New();
RigidTransformType::ParametersType parameters(RigidTransformType::ParametersDimension);
for (int i = 0; i<6;++i)
{
parameters[i] = transformation[i];
}
rtransform->SetParameters( parameters );
mitk::Point3D b0origin = diffImages->GetVectorImage()->GetOrigin();
b0origin[0]-=offset[0];
b0origin[1]-=offset[1];
b0origin[2]-=offset[2];
mitk::Point3D newOrigin = rtransform->GetInverseTransform()->TransformPoint(b0origin);
itk::Matrix<double,3,3> dir = diffImages->GetVectorImage()->GetDirection();
itk::Matrix<double,3,3> transM ( vnl_inverse(rtransform->GetMatrix().GetVnlMatrix()));
itk::Matrix<double,3,3> newDirection = transM * dir;
diffImages->GetVectorImage()->SetOrigin(newOrigin);
diffImages->GetVectorImage()->SetDirection(newDirection);
diffImages->Modified();
mitk::DiffusionImageCorrectionFilter<short>::Pointer correctionFilter = mitk::DiffusionImageCorrectionFilter<short>::New();
// For Diff. Images: Need to rotate the gradients (works in-place)
correctionFilter->SetImage(diffImages);
correctionFilter->CorrectDirections(transM.GetVnlMatrix());
img = diffImages;
}
}
/**
* Denoises DWI using the Nonlocal - Means algorithm
*/
int main(int argc, char* argv[])
{
mitkCommandLineParser parser;
parser.setTitle("DWI Denoising");
parser.setCategory("Preprocessing Tools");
parser.setContributor("MIC");
parser.setDescription("Denoising for diffusion weighted images using a non-local means algorithm.");
parser.setArgumentPrefix("--", "-");
parser.addArgument("input", "i", mitkCommandLineParser::InputFile, "Input:", "input image (DWI)", us::Any(), false);
parser.addArgument("variance", "v", mitkCommandLineParser::Float, "Variance:", "noise variance", us::Any(), false);
parser.addArgument("mask", "m", mitkCommandLineParser::InputFile, "Mask:", "brainmask for input image", us::Any(), true);
parser.addArgument("search", "s", mitkCommandLineParser::Int, "Search radius:", "search radius", us::Any(), true);
parser.addArgument("compare", "c", mitkCommandLineParser::Int, "Comparison radius:", "comparison radius", us::Any(), true);
parser.addArgument("joint", "j", mitkCommandLineParser::Bool, "Joint information:", "use joint information");
parser.addArgument("rician", "r", mitkCommandLineParser::Bool, "Rician adaption:", "use rician adaption");
parser.changeParameterGroup("Output", "Output of this miniapp");
parser.addArgument("output", "o", mitkCommandLineParser::OutputFile, "Output:", "output image (DWI)", us::Any(), false);
map<string, us::Any> parsedArgs = parser.parseArguments(argc, argv);
if (parsedArgs.size()==0)
return EXIT_FAILURE;
string inFileName = us::any_cast<string>(parsedArgs["input"]);
double variance = static_cast<double>(us::any_cast<float>(parsedArgs["variance"]));
string maskName;
if (parsedArgs.count("mask"))
maskName = us::any_cast<string>(parsedArgs["mask"]);
string outFileName = us::any_cast<string>(parsedArgs["output"]);
// boost::algorithm::erase_all(outFileName, ".dwi");
int search = 4;
if (parsedArgs.count("search"))
search = us::any_cast<int>(parsedArgs["search"]);
int compare = 1;
if (parsedArgs.count("compare"))
compare = us::any_cast<int>(parsedArgs["compare"]);
bool joint = false;
if (parsedArgs.count("joint"))
joint = true;
bool rician = false;
if (parsedArgs.count("rician"))
rician = true;
try
{
mitk::PreferenceListReaderOptionsFunctor functor = mitk::PreferenceListReaderOptionsFunctor({"Diffusion Weighted Images"}, {});
DiffusionImageType::Pointer dwi = dynamic_cast<mitk::Image*>(mitk::IOUtil::LoadImage(inFileName, &functor).GetPointer());
mitk::DiffusionPropertyHelper::ImageType::Pointer itkVectorImagePointer = mitk::DiffusionPropertyHelper::ImageType::New();
mitk::CastToItkImage(dwi, itkVectorImagePointer);
itk::NonLocalMeansDenoisingFilter<short>::Pointer filter = itk::NonLocalMeansDenoisingFilter<short>::New();
filter->SetNumberOfThreads(12);
filter->SetInputImage( itkVectorImagePointer );
if (!maskName.empty())
{
mitk::Image::Pointer mask = dynamic_cast<mitk::Image*>(mitk::IOUtil::Load(maskName)[0].GetPointer());
ImageType::Pointer itkMask = ImageType::New();
mitk::CastToItkImage(mask, itkMask);
filter->SetInputMask(itkMask);
}
filter->SetUseJointInformation(joint);
filter->SetUseRicianAdaption(rician);
filter->SetSearchRadius(search);
filter->SetComparisonRadius(compare);
filter->SetVariance(variance);
filter->Update();
DiffusionImageType::Pointer output = mitk::GrabItkImageMemory( filter->GetOutput() );
output->GetPropertyList()->ReplaceProperty( mitk::DiffusionPropertyHelper::REFERENCEBVALUEPROPERTYNAME.c_str(), mitk::FloatProperty::New( mitk::DiffusionPropertyHelper::GetReferenceBValue(dwi) ) );
output->GetPropertyList()->ReplaceProperty( mitk::DiffusionPropertyHelper::GRADIENTCONTAINERPROPERTYNAME.c_str(), mitk::GradientDirectionsProperty::New( mitk::DiffusionPropertyHelper::GetGradientContainer(dwi) ) );
mitk::DiffusionPropertyHelper propertyHelper( output );
propertyHelper.InitializeImage();
// std::stringstream name;
// name << outFileName << "_NLM_" << search << "-" << compare << "-" << variance << ".dwi";
mitk::IOUtil::Save(output, outFileName.c_str());
}
catch (itk::ExceptionObject e)
{
std::cout << e;
return EXIT_FAILURE;
}
catch (std::exception e)
{
std::cout << e.what();
return EXIT_FAILURE;
}
catch (...)
{
std::cout << "ERROR!?!";
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
void QmitkDenoisingView::StartDenoising()
{
if (!m_ThreadIsRunning)
{
if (m_ImageNode.IsNotNull())
{
m_LastProgressCount = 0;
switch (m_SelectedFilter)
{
case NOFILTERSELECTED:
{
break;
}
case NLM:
{
// initialize NLM
m_InputImage = dynamic_cast<DiffusionImageType*> (m_ImageNode->GetData());
m_NonLocalMeansFilter = NonLocalMeansDenoisingFilterType::New();
if (m_BrainMaskNode.IsNotNull())
{
// use brainmask if set
m_ImageMask = dynamic_cast<mitk::Image*>(m_BrainMaskNode->GetData());
itk::Image<DiffusionPixelType, 3>::Pointer itkMask;
mitk::CastToItkImage(m_ImageMask, itkMask);
m_NonLocalMeansFilter->SetInputMask(itkMask);
itk::ImageRegionIterator< itk::Image<DiffusionPixelType, 3> > mit(itkMask, itkMask->GetLargestPossibleRegion());
mit.GoToBegin();
itk::Image<DiffusionPixelType, 3>::IndexType minIndex;
itk::Image<DiffusionPixelType, 3>::IndexType maxIndex;
minIndex.Fill(10000);
maxIndex.Fill(0);
while (!mit.IsAtEnd())
{
if (mit.Get())
{
// calculation of the start & end index of the smallest masked region
minIndex[0] = minIndex[0] < mit.GetIndex()[0] ? minIndex[0] : mit.GetIndex()[0];
minIndex[1] = minIndex[1] < mit.GetIndex()[1] ? minIndex[1] : mit.GetIndex()[1];
minIndex[2] = minIndex[2] < mit.GetIndex()[2] ? minIndex[2] : mit.GetIndex()[2];
maxIndex[0] = maxIndex[0] > mit.GetIndex()[0] ? maxIndex[0] : mit.GetIndex()[0];
maxIndex[1] = maxIndex[1] > mit.GetIndex()[1] ? maxIndex[1] : mit.GetIndex()[1];
maxIndex[2] = maxIndex[2] > mit.GetIndex()[2] ? maxIndex[2] : mit.GetIndex()[2];
}
++mit;
}
itk::Image<DiffusionPixelType, 3>::SizeType size;
size[0] = maxIndex[0] - minIndex[0] + 1;
size[1] = maxIndex[1] - minIndex[1] + 1;
size[2] = maxIndex[2] - minIndex[2] + 1;
m_MaxProgressCount = size[0] * size[1] * size[2];
}
else
{
// initialize the progressbar
m_MaxProgressCount = m_InputImage->GetDimension(0) * m_InputImage->GetDimension(1) * m_InputImage->GetDimension(2);
}
mitk::ProgressBar::GetInstance()->AddStepsToDo(m_MaxProgressCount);
m_NonLocalMeansFilter->SetInputImage(m_InputImage->GetVectorImage());
m_NonLocalMeansFilter->SetUseRicianAdaption(m_Controls->m_RicianCheckbox->isChecked());
m_NonLocalMeansFilter->SetUseJointInformation(m_Controls->m_JointInformationCheckbox->isChecked());
m_NonLocalMeansFilter->SetSearchRadius(m_Controls->m_SpinBoxParameter1->value());
m_NonLocalMeansFilter->SetComparisonRadius(m_Controls->m_SpinBoxParameter2->value());
m_NonLocalMeansFilter->SetVariance(m_Controls->m_DoubleSpinBoxParameter3->value());
// start denoising in detached thread
m_DenoisingThread.start(QThread::HighestPriority);
break;
}
case GAUSS:
{
// initialize GAUSS and run
m_InputImage = dynamic_cast<DiffusionImageType*> (m_ImageNode->GetData());
ExtractFilterType::Pointer extractor = ExtractFilterType::New();
extractor->SetInput(m_InputImage->GetVectorImage());
ComposeFilterType::Pointer composer = ComposeFilterType::New();
for (unsigned int i = 0; i < m_InputImage->GetVectorImage()->GetVectorLength(); ++i)
{
extractor->SetIndex(i);
extractor->Update();
m_GaussianFilter = GaussianFilterType::New();
m_GaussianFilter->SetVariance(m_Controls->m_SpinBoxParameter1->value());
if (m_BrainMaskNode.IsNotNull())
{
m_ImageMask = dynamic_cast<mitk::Image*>(m_BrainMaskNode->GetData());
itk::Image<DiffusionPixelType, 3>::Pointer itkMask = itk::Image<DiffusionPixelType, 3>::New();
mitk::CastToItkImage(m_ImageMask, itkMask);
itk::MaskImageFilter<itk::Image<DiffusionPixelType, 3> , itk::Image<DiffusionPixelType, 3> >::Pointer maskImageFilter = itk::MaskImageFilter<itk::Image<DiffusionPixelType, 3> , itk::Image<DiffusionPixelType, 3> >::New();
maskImageFilter->SetInput(extractor->GetOutput());
maskImageFilter->SetMaskImage(itkMask);
maskImageFilter->Update();
m_GaussianFilter->SetInput(maskImageFilter->GetOutput());
}
else
{
m_GaussianFilter->SetInput(extractor->GetOutput());
}
m_GaussianFilter->Update();
composer->SetInput(i, m_GaussianFilter->GetOutput());
}
composer->Update();
DiffusionImageType::Pointer image = DiffusionImageType::New();
image->SetVectorImage(composer->GetOutput());
image->SetReferenceBValue(m_InputImage->GetReferenceBValue());
image->SetDirections(m_InputImage->GetDirections());
image->InitializeFromVectorImage();
mitk::DataNode::Pointer imageNode = mitk::DataNode::New();
imageNode->SetData( image );
QString name = m_ImageNode->GetName().c_str();
imageNode->SetName((name+"_gauss_"+QString::number(m_Controls->m_SpinBoxParameter1->value())).toStdString().c_str());
GetDefaultDataStorage()->Add(imageNode);
break;
}
}
}
}
else
{
m_NonLocalMeansFilter->AbortGenerateDataOn();
m_CompletedCalculation = false;
}
}
