int Invert( int argc , char* argv[] )
{
//check arguments
if( argc != 4 )
{
std::cout<< argv[ 0 ] << " " << argv[ 1 ] << " inputTransform outputTransform" << std::endl ;
return 1 ;
}
typedef itk::MatrixOffsetTransformBase< double , 3 , 3 > TransformType ;
itk::TransformFactory< TransformType >::RegisterTransform();
itk::TransformFileReader::Pointer transformFile = itk::TransformFileReader::New() ;
transformFile->SetFileName( argv[ 2 ] ) ;
transformFile->Update() ;
if( transformFile->GetTransformList()->size() != 1 )
{
std::cerr << "Please give a transform file containing only one transformation" << std::endl ;
return 1 ;
}
TransformType::Pointer transform ;
transform = dynamic_cast< TransformType* >
( transformFile->GetTransformList()->front().GetPointer() ) ;
if( !transform )
{
std::cerr << "Transform type is not handled. Please convert your transform first" << std::endl ;
return 1 ;
}
TransformType::Pointer inverse = TransformType::New() ;
transform->GetInverse( inverse ) ;
itk::TransformFileWriter::Pointer transformWriter = itk::TransformFileWriter::New() ;
transformWriter->SetFileName( argv[ 3 ] ) ;
transformWriter->AddTransform( inverse ) ;
transformWriter->Update() ;
return 0 ;
}
mitk::Vector3D
mitk::SlicedGeometry3D::AdjustNormal( const mitk::Vector3D &normal ) const
{
TransformType::Pointer inverse = TransformType::New();
m_ReferenceGeometry->GetIndexToWorldTransform()->GetInverse( inverse );
Vector3D transformedNormal = inverse->TransformVector( normal );
transformedNormal.Normalize();
return transformedNormal;
}
void initialiseFilters() {
// resamplers
transform = TransformType::New();
transform->SetIdentity();
volumeInterpolator = VolumeInterpolatorType::New();
maskVolumeInterpolator = MaskVolumeInterpolatorType::New();
resampler = ResamplerType::New();
resampler->SetInput( originalImage );
resampler->SetInterpolator( volumeInterpolator );
resampler->SetOutputSpacing( resamplerSpacing );
resampler->SetSize( resamplerSize );
resampler->SetTransform( transform );
resampler->SetDefaultPixelValue( 127 );
maskResampler = MaskResamplerType::New();
maskResampler->SetInput( originalMask );
maskResampler->SetInterpolator( maskVolumeInterpolator );
maskResampler->SetOutputSpacing( resamplerSpacing );
maskResampler->SetSize( resamplerSize );
maskResampler->SetTransform( transform );
// extract image filters
sliceExtractor = SliceExtractorType::New();
sliceExtractor->SetInput( resampler->GetOutput() );
maskSliceExtractor = MaskSliceExtractorType::New();
maskSliceExtractor->SetInput( maskResampler->GetOutput() );
// masks
for(unsigned int i=0; i<resamplerSize[2]; i++) {
masks2D.push_back( MaskType2D::New() );
}
}
int main(int argc, char ** argv)
{
// load the image and the bounding box
BoundingBox::Pointer boundingBox = BoundingBox::New();
boundingBox->SetInfation(atof(argv[3]));
boundingBox->Load(argv[1]);
// load hte images and compute the reference coordinates
CMRFileExtractor::Pointer extractor = CMRFileExtractor::New();
extractor->SetFolderName(argv[2]);
extractor->Extract();
ValveOriginFinder::Pointer originFinder = ValveOriginFinder::New();
originFinder->Set2CImage(extractor->Get2CImage(0));
originFinder->Set3CImage(extractor->Get3CImage(0));
originFinder->SetImageStack(extractor->GetStackImage(0));
originFinder->Compute();
// apply the transform to the bounding box
typedef itk::Similarity3DTransform<double> TransformType;
TransformType::Pointer transform = TransformType::New();
transform->SetMatrix(originFinder->GetRotation());
transform->SetTranslation(originFinder->GetTranslation());
boundingBox->TransformBoundingBox(transform);
BoundingBox::MaskType::Pointer mask = BoundingBox::MaskType::New();
boundingBox->ComputeImageMask(extractor->Get2CImage(0), 1, mask);
utils::LabelVolumeIO::Write("mask.nrrd", mask);
utils::ImageVolumeIO::Write("image.nrrd", extractor->Get2CImage(0));
SimpleMRFSegmenter::Pointer segmenter = SimpleMRFSegmenter::New();
segmenter->SetImage(extractor->Get2CImage(0));
segmenter->SetSmoothnessCost(atof(argv[4]));
segmenter->SetMask(mask);
segmenter->Segment();
utils::LabelVolumeIO::Write("seg.nrrd", segmenter->GetOutput());
return 0;
}
void ImageScaleTransform::process()
{
foreach (const ElementBase *source, mSourceElementsReadySet)
for (int i = 0; i < source->getFramesNo(); ++i)
{
const FrameBase *frame = source->getFrame(i);
if (frame->getMaxDimension() == ColorImageFrame::Dimensions)
{
mImageFrame.setSourceName(frame->getSourceName());
mSrcFrame.resizeAndCopyFrame(*frame);
ColorImageFrame::ImageType::Pointer srcImg = mSrcFrame;
typedef ScaleTransform<double, 2> TransformType;
TransformType::Pointer scaleTransform = TransformType::New();
FixedArray<float, 2> scale;
scale[0] = property("widthScale").toDouble();
scale[1] = property("heightScale").toDouble();
scaleTransform->SetScale(scale);
Point<float, 2> center;
center[0] = srcImg->GetLargestPossibleRegion().GetSize()[0]/2;
center[1] = srcImg->GetLargestPossibleRegion().GetSize()[1]/2;
scaleTransform->SetCenter(center);
typedef ResampleImageFilter<ColorImageFrame::ImageType, ColorImageFrame::ImageType> ResampleImageFilterType;
ResampleImageFilterType::Pointer resampleFilter = ResampleImageFilterType::New();
resampleFilter->SetTransform(scaleTransform);
resampleFilter->SetInput(srcImg);
resampleFilter->SetSize(srcImg->GetLargestPossibleRegion().GetSize());
resampleFilter->Update();
mImageFrame = resampleFilter->GetOutput();
emit framesReady();
break;
}
}
}
void QAngioSubstractionExtension::computeAutomateSingleImage()
{
QApplication::setOverrideCursor(Qt::WaitCursor);
const unsigned int Dimension = 2;
typedef Volume::ItkPixelType PixelType;
typedef itk::Image< PixelType, Dimension > FixedImageType;
typedef itk::Image< PixelType, Dimension > MovingImageType;
typedef float InternalPixelType;
typedef itk::Image< InternalPixelType, Dimension > InternalImageType;
typedef itk::TranslationTransform< double, Dimension > TransformType;
typedef itk::GradientDescentOptimizer OptimizerType;
typedef itk::LinearInterpolateImageFunction<
InternalImageType,
double > InterpolatorType;
typedef itk::ImageRegistrationMethod<
InternalImageType,
InternalImageType > RegistrationType;
typedef itk::MutualInformationImageToImageMetric<
InternalImageType,
InternalImageType > MetricType;
TransformType::Pointer transform = TransformType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetOptimizer(optimizer);
registration->SetTransform(transform);
registration->SetInterpolator(interpolator);
MetricType::Pointer metric = MetricType::New();
registration->SetMetric(metric);
metric->SetFixedImageStandardDeviation(0.4);
metric->SetMovingImageStandardDeviation(0.4);
metric->SetNumberOfSpatialSamples(50);
typedef itk::ExtractImageFilter< Volume::ItkImageType, FixedImageType > FilterType;
FilterType::Pointer extractFixedImageFilter = FilterType::New();
Volume::ItkImageType::RegionType inputRegion = m_mainVolume->getItkData()->GetLargestPossibleRegion();
Volume::ItkImageType::SizeType size = inputRegion.GetSize();
//Dividim la mida per dos per tal de quedar-nos només amb la part central
// ja que si no ens registre el background
size[0] = size[0] / 2;
size[1] = size[1] / 2;
size[2] = 0;
Volume::ItkImageType::IndexType start = inputRegion.GetIndex();
const unsigned int sliceReference = m_imageSelectorSpinBox->value();
//comencem a un quart de la imatge
start[0] = size[0] / 2;
start[1] = size[1] / 2;
start[2] = sliceReference;
Volume::ItkImageType::RegionType desiredRegion;
desiredRegion.SetSize(size);
desiredRegion.SetIndex(start);
extractFixedImageFilter->SetExtractionRegion(desiredRegion);
extractFixedImageFilter->SetInput(m_mainVolume->getItkData());
extractFixedImageFilter->Update();
FilterType::Pointer extractMovingImageFilter = FilterType::New();
Volume::ItkImageType::IndexType startMoving = inputRegion.GetIndex();
const unsigned int sliceNumber = m_2DView_1->getViewer()->getCurrentSlice();
startMoving[0] = size[0] / 2;
startMoving[1] = size[1] / 2;
startMoving[2] = sliceNumber;
Volume::ItkImageType::RegionType desiredMovingRegion;
desiredMovingRegion.SetSize(size);
desiredMovingRegion.SetIndex(startMoving);
extractMovingImageFilter->SetExtractionRegion(desiredMovingRegion);
extractMovingImageFilter->SetInput(m_mainVolume->getItkData());
extractMovingImageFilter->Update();
typedef itk::NormalizeImageFilter<
FixedImageType,
InternalImageType
> FixedNormalizeFilterType;
typedef itk::NormalizeImageFilter<
MovingImageType,
InternalImageType
> MovingNormalizeFilterType;
FixedNormalizeFilterType::Pointer fixedNormalizer =
FixedNormalizeFilterType::New();
MovingNormalizeFilterType::Pointer movingNormalizer =
MovingNormalizeFilterType::New();
typedef itk::DiscreteGaussianImageFilter<
InternalImageType,
InternalImageType
> GaussianFilterType;
GaussianFilterType::Pointer fixedSmoother = GaussianFilterType::New();
GaussianFilterType::Pointer movingSmoother = GaussianFilterType::New();
fixedSmoother->SetVariance(2.0);
movingSmoother->SetVariance(2.0);
fixedNormalizer->SetInput(extractFixedImageFilter->GetOutput());
movingNormalizer->SetInput(extractMovingImageFilter->GetOutput());
fixedSmoother->SetInput(fixedNormalizer->GetOutput());
movingSmoother->SetInput(movingNormalizer->GetOutput());
registration->SetFixedImage(fixedSmoother->GetOutput());
registration->SetMovingImage(movingSmoother->GetOutput());
fixedNormalizer->Update();
registration->SetFixedImageRegion(
fixedNormalizer->GetOutput()->GetBufferedRegion());
typedef RegistrationType::ParametersType ParametersType;
ParametersType initialParameters(transform->GetNumberOfParameters());
initialParameters[0] = 0.0; // Initial offset in mm along X
initialParameters[1] = 0.0; // Initial offset in mm along Y
registration->SetInitialTransformParameters(initialParameters);
optimizer->SetLearningRate(20.0);
optimizer->SetNumberOfIterations(200);
optimizer->MaximizeOn();
try
{
registration->Update();
}
catch(itk::ExceptionObject & err)
{
std::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return;
}
ParametersType finalParameters = registration->GetLastTransformParameters();
double TranslationAlongX = finalParameters[0];
double TranslationAlongY = finalParameters[1];
// Print out results
//
DEBUG_LOG(QString("Result = "));
DEBUG_LOG(QString(" Translation X = %1").arg(TranslationAlongX));
DEBUG_LOG(QString(" Translation Y = %1").arg(TranslationAlongY));
DEBUG_LOG(QString(" Iterations = %1").arg(optimizer->GetCurrentIteration()));
DEBUG_LOG(QString(" Metric value = %1").arg(optimizer->GetValue()));
double spacing[3];
m_mainVolume->getSpacing(spacing);
DEBUG_LOG(QString(" Translation X (in px) = %1").arg(TranslationAlongX / spacing[0]));
DEBUG_LOG(QString(" Translation Y (in px) = %1").arg(TranslationAlongY / spacing[1]));
//Actualitzem les dades de la transdifference tool
m_toolManager->triggerTool("TransDifferenceTool");
TransDifferenceTool* tdTool = static_cast<TransDifferenceTool*> (m_2DView_2->getViewer()->getToolProxy()->getTool("TransDifferenceTool"));
if(m_tdToolData == 0){
m_tdToolData = static_cast<TransDifferenceToolData*> (tdTool->getToolData());
}
if(m_tdToolData->getInputVolume() != m_mainVolume){
m_tdToolData->setInputVolume(m_mainVolume);
}
tdTool->setSingleDifferenceImage(TranslationAlongX / spacing[0],TranslationAlongY / spacing[1]);
m_toolManager->triggerTool("SlicingTool");
/* typedef itk::Image< PixelType, Dimension > FixedImageType;
typedef itk::Image< PixelType, Dimension > MovingImageType;
typedef itk::TranslationTransform< double, Dimension > TransformType;
typedef itk::RegularStepGradientDescentOptimizer OptimizerType;
typedef itk::MattesMutualInformationImageToImageMetric<
FixedImageType,
MovingImageType > MetricType;
typedef itk:: LinearInterpolateImageFunction<
MovingImageType,
double > InterpolatorType;
typedef itk::ImageRegistrationMethod<
FixedImageType,
MovingImageType > RegistrationType;
MetricType::Pointer metric = MetricType::New();
TransformType::Pointer transform = TransformType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetMetric(metric);
registration->SetOptimizer(optimizer);
registration->SetTransform(transform);
registration->SetInterpolator(interpolator);
metric->SetNumberOfHistogramBins(50);
metric->SetNumberOfSpatialSamples(10000);
typedef itk::ExtractImageFilter< Volume::ItkImageType, FixedImageType > FilterType;
FilterType::Pointer extractFixedImageFilter = FilterType::New();
Volume::ItkImageType::RegionType inputRegion = m_mainVolume->getItkData()->GetLargestPossibleRegion();
Volume::ItkImageType::SizeType size = inputRegion.GetSize();
//Dividim la mida per dos per tal de quedar-nos només amb la part central
// ja que si no ens registre el background
size[0] = size[0] / 2;
size[1] = size[1] / 2;
size[2] = 0;
Volume::ItkImageType::IndexType start = inputRegion.GetIndex();
const unsigned int sliceReference = m_imageSelectorSpinBox->value();
//comencem a un quart de la imatge
start[0] = size[0] / 2;
start[1] = size[1] / 2;
start[2] = sliceReference;
Volume::ItkImageType::RegionType desiredRegion;
desiredRegion.SetSize(size);
desiredRegion.SetIndex(start);
extractFixedImageFilter->SetExtractionRegion(desiredRegion);
extractFixedImageFilter->SetInput(m_mainVolume->getItkData());
extractFixedImageFilter->Update();
FilterType::Pointer extractMovingImageFilter = FilterType::New();
Volume::ItkImageType::IndexType startMoving = inputRegion.GetIndex();
const unsigned int sliceNumber = m_2DView_1->getViewer()->getCurrentSlice();
startMoving[0] = size[0] / 2;
startMoving[1] = size[1] / 2;
startMoving[2] = sliceNumber;
Volume::ItkImageType::RegionType desiredMovingRegion;
desiredMovingRegion.SetSize(size);
desiredMovingRegion.SetIndex(startMoving);
extractMovingImageFilter->SetExtractionRegion(desiredMovingRegion);
extractMovingImageFilter->SetInput(m_mainVolume->getItkData());
extractMovingImageFilter->Update();
registration->SetFixedImage(extractFixedImageFilter->GetOutput());
registration->SetMovingImage(extractMovingImageFilter->GetOutput());
typedef RegistrationType::ParametersType ParametersType;
ParametersType initialParameters(transform->GetNumberOfParameters());
//Potser seria millor posar la transformada que té actualment
initialParameters[0] = 0.0; // Initial offset in mm along X
initialParameters[1] = 0.0; // Initial offset in mm along Y
registration->SetInitialTransformParameters(initialParameters);
optimizer->SetMaximumStepLength(4.00);
optimizer->SetMinimumStepLength(0.005);
optimizer->SetNumberOfIterations(200);
try
{
registration->StartRegistration();
}
catch(itk::ExceptionObject & err)
{
DEBUG_LOG(QString("ExceptionObject caught !"));
std::cout<<err<<std::endl;
return;
}
ParametersType finalParameters = registration->GetLastTransformParameters();
const double TranslationAlongX = finalParameters[0];
const double TranslationAlongY = finalParameters[1];
const unsigned int numberOfIterations = optimizer->GetCurrentIteration();
const double bestValue = optimizer->GetValue();
DEBUG_LOG(QString("Result = "));
DEBUG_LOG(QString(" Translation X = %1").arg(TranslationAlongX));
DEBUG_LOG(QString(" Translation Y = %1").arg(TranslationAlongY));
DEBUG_LOG(QString(" Iterations = %1").arg(numberOfIterations));
DEBUG_LOG(QString(" Metric value = %1").arg(bestValue));
typedef unsigned char OutputPixelType;
typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
typedef itk::RescaleIntensityImageFilter< FixedImageType, FixedImageType > RescaleFilterType;
typedef itk::ResampleImageFilter<
FixedImageType,
FixedImageType > ResampleFilterType;
typedef itk::CastImageFilter<
FixedImageType,
OutputImageType > CastFilterType;
typedef itk::ImageFileWriter< OutputImageType > WriterType;
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
ResampleFilterType::Pointer resample = ResampleFilterType::New();
RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum(0);
rescaler->SetOutputMaximum(255);
TransformType::Pointer finalTransform = TransformType::New();
finalTransform->SetParameters(finalParameters);
resample->SetTransform(finalTransform);
resample->SetSize(extractMovingImageFilter->GetOutput()->GetLargestPossibleRegion().GetSize());
resample->SetOutputOrigin(extractMovingImageFilter->GetOutput()->GetOrigin());
resample->SetOutputSpacing(extractMovingImageFilter->GetOutput()->GetSpacing());
resample->SetDefaultPixelValue(100);
writer->SetFileName("prova.jpg");
rescaler->SetInput(extractMovingImageFilter->GetOutput());
resample->SetInput(rescaler->GetOutput());
caster->SetInput(resample->GetOutput());
writer->SetInput(caster->GetOutput());
writer->Update();
*/
QApplication::restoreOverrideCursor();
}
bool ShowSegmentationAsSmoothedSurface::ThreadedUpdateFunction()
{
Image::Pointer image;
GetPointerParameter("Input", image);
float smoothing;
GetParameter("Smoothing", smoothing);
float decimation;
GetParameter("Decimation", decimation);
float closing;
GetParameter("Closing", closing);
int timeNr = 0;
GetParameter("TimeNr", timeNr);
if (image->GetDimension() == 4)
MITK_INFO << "CREATING SMOOTHED POLYGON MODEL (t = " << timeNr << ')';
else
MITK_INFO << "CREATING SMOOTHED POLYGON MODEL";
MITK_INFO << " Smoothing = " << smoothing;
MITK_INFO << " Decimation = " << decimation;
MITK_INFO << " Closing = " << closing;
Geometry3D::Pointer geometry = dynamic_cast<Geometry3D *>(image->GetGeometry()->Clone().GetPointer());
// Make ITK image out of MITK image
typedef itk::Image<unsigned char, 3> CharImageType;
typedef itk::Image<unsigned short, 3> ShortImageType;
typedef itk::Image<float, 3> FloatImageType;
if (image->GetDimension() == 4)
{
ImageTimeSelector::Pointer imageTimeSelector = ImageTimeSelector::New();
imageTimeSelector->SetInput(image);
imageTimeSelector->SetTimeNr(timeNr);
imageTimeSelector->UpdateLargestPossibleRegion();
image = imageTimeSelector->GetOutput(0);
}
ImageToItk<CharImageType>::Pointer imageToItkFilter = ImageToItk<CharImageType>::New();
try
{
imageToItkFilter->SetInput(image);
}
catch (const itk::ExceptionObject &e)
{
// Most probably the input image type is wrong. Binary images are expected to be
// >unsigned< char images.
MITK_ERROR << e.GetDescription() << endl;
return false;
}
imageToItkFilter->Update();
CharImageType::Pointer itkImage = imageToItkFilter->GetOutput();
// Get bounding box and relabel
MITK_INFO << "Extracting VOI...";
int imageLabel = 1;
bool roiFound = false;
CharImageType::IndexType minIndex;
minIndex.Fill(numeric_limits<CharImageType::IndexValueType>::max());
CharImageType::IndexType maxIndex;
maxIndex.Fill(numeric_limits<CharImageType::IndexValueType>::min());
itk::ImageRegionIteratorWithIndex<CharImageType> iter(itkImage, itkImage->GetLargestPossibleRegion());
for (iter.GoToBegin(); !iter.IsAtEnd(); ++iter)
{
if (iter.Get() == imageLabel)
{
roiFound = true;
iter.Set(1);
CharImageType::IndexType currentIndex = iter.GetIndex();
for (unsigned int dim = 0; dim < 3; ++dim)
{
minIndex[dim] = min(currentIndex[dim], minIndex[dim]);
maxIndex[dim] = max(currentIndex[dim], maxIndex[dim]);
}
}
else
{
iter.Set(0);
}
}
if (!roiFound)
{
ProgressBar::GetInstance()->Progress(8);
MITK_ERROR << "Didn't found segmentation labeled with " << imageLabel << "!" << endl;
return false;
}
ProgressBar::GetInstance()->Progress(1);
// Extract and pad bounding box
typedef itk::RegionOfInterestImageFilter<CharImageType, CharImageType> ROIFilterType;
ROIFilterType::Pointer roiFilter = ROIFilterType::New();
CharImageType::RegionType region;
CharImageType::SizeType size;
for (unsigned int dim = 0; dim < 3; ++dim)
{
size[dim] = maxIndex[dim] - minIndex[dim] + 1;
}
region.SetIndex(minIndex);
region.SetSize(size);
roiFilter->SetInput(itkImage);
roiFilter->SetRegionOfInterest(region);
roiFilter->ReleaseDataFlagOn();
roiFilter->ReleaseDataBeforeUpdateFlagOn();
typedef itk::ConstantPadImageFilter<CharImageType, CharImageType> PadFilterType;
PadFilterType::Pointer padFilter = PadFilterType::New();
const PadFilterType::SizeValueType pad[3] = { 10, 10, 10 };
padFilter->SetInput(roiFilter->GetOutput());
padFilter->SetConstant(0);
padFilter->SetPadLowerBound(pad);
padFilter->SetPadUpperBound(pad);
padFilter->ReleaseDataFlagOn();
padFilter->ReleaseDataBeforeUpdateFlagOn();
padFilter->Update();
CharImageType::Pointer roiImage = padFilter->GetOutput();
roiImage->DisconnectPipeline();
roiFilter = nullptr;
padFilter = nullptr;
// Correct origin of real geometry (changed by cropping and padding)
typedef Geometry3D::TransformType TransformType;
TransformType::Pointer transform = TransformType::New();
TransformType::OutputVectorType translation;
for (unsigned int dim = 0; dim < 3; ++dim)
translation[dim] = (int)minIndex[dim] - (int)pad[dim];
transform->SetIdentity();
transform->Translate(translation);
geometry->Compose(transform, true);
ProgressBar::GetInstance()->Progress(1);
// Median
MITK_INFO << "Median...";
typedef itk::BinaryMedianImageFilter<CharImageType, CharImageType> MedianFilterType;
MedianFilterType::Pointer medianFilter = MedianFilterType::New();
CharImageType::SizeType radius = { 0 };
medianFilter->SetRadius(radius);
medianFilter->SetBackgroundValue(0);
medianFilter->SetForegroundValue(1);
medianFilter->SetInput(roiImage);
medianFilter->ReleaseDataFlagOn();
medianFilter->ReleaseDataBeforeUpdateFlagOn();
medianFilter->Update();
ProgressBar::GetInstance()->Progress(1);
// Intelligent closing
MITK_INFO << "Intelligent closing...";
unsigned int surfaceRatio = (unsigned int)((1.0f - closing) * 100.0f);
typedef itk::IntelligentBinaryClosingFilter<CharImageType, ShortImageType> ClosingFilterType;
ClosingFilterType::Pointer closingFilter = ClosingFilterType::New();
closingFilter->SetInput(medianFilter->GetOutput());
closingFilter->ReleaseDataFlagOn();
closingFilter->ReleaseDataBeforeUpdateFlagOn();
closingFilter->SetSurfaceRatio(surfaceRatio);
closingFilter->Update();
ShortImageType::Pointer closedImage = closingFilter->GetOutput();
closedImage->DisconnectPipeline();
roiImage = nullptr;
medianFilter = nullptr;
closingFilter = nullptr;
ProgressBar::GetInstance()->Progress(1);
// Gaussian blur
MITK_INFO << "Gauss...";
typedef itk::BinaryThresholdImageFilter<ShortImageType, FloatImageType> BinaryThresholdToFloatFilterType;
BinaryThresholdToFloatFilterType::Pointer binThresToFloatFilter = BinaryThresholdToFloatFilterType::New();
binThresToFloatFilter->SetInput(closedImage);
binThresToFloatFilter->SetLowerThreshold(1);
binThresToFloatFilter->SetUpperThreshold(1);
binThresToFloatFilter->SetInsideValue(100);
binThresToFloatFilter->SetOutsideValue(0);
binThresToFloatFilter->ReleaseDataFlagOn();
binThresToFloatFilter->ReleaseDataBeforeUpdateFlagOn();
typedef itk::DiscreteGaussianImageFilter<FloatImageType, FloatImageType> GaussianFilterType;
// From the following line on, IntelliSense (VS 2008) is broken. Any idea how to fix it?
GaussianFilterType::Pointer gaussFilter = GaussianFilterType::New();
gaussFilter->SetInput(binThresToFloatFilter->GetOutput());
gaussFilter->SetUseImageSpacing(true);
gaussFilter->SetVariance(smoothing);
gaussFilter->ReleaseDataFlagOn();
gaussFilter->ReleaseDataBeforeUpdateFlagOn();
typedef itk::BinaryThresholdImageFilter<FloatImageType, CharImageType> BinaryThresholdFromFloatFilterType;
BinaryThresholdFromFloatFilterType::Pointer binThresFromFloatFilter = BinaryThresholdFromFloatFilterType::New();
binThresFromFloatFilter->SetInput(gaussFilter->GetOutput());
binThresFromFloatFilter->SetLowerThreshold(50);
binThresFromFloatFilter->SetUpperThreshold(255);
binThresFromFloatFilter->SetInsideValue(1);
binThresFromFloatFilter->SetOutsideValue(0);
binThresFromFloatFilter->ReleaseDataFlagOn();
binThresFromFloatFilter->ReleaseDataBeforeUpdateFlagOn();
binThresFromFloatFilter->Update();
CharImageType::Pointer blurredImage = binThresFromFloatFilter->GetOutput();
blurredImage->DisconnectPipeline();
closedImage = nullptr;
binThresToFloatFilter = nullptr;
gaussFilter = nullptr;
ProgressBar::GetInstance()->Progress(1);
// Fill holes
MITK_INFO << "Filling cavities...";
typedef itk::ConnectedThresholdImageFilter<CharImageType, CharImageType> ConnectedThresholdFilterType;
ConnectedThresholdFilterType::Pointer connectedThresFilter = ConnectedThresholdFilterType::New();
CharImageType::IndexType corner;
corner[0] = 0;
corner[1] = 0;
corner[2] = 0;
connectedThresFilter->SetInput(blurredImage);
connectedThresFilter->SetSeed(corner);
connectedThresFilter->SetLower(0);
connectedThresFilter->SetUpper(0);
connectedThresFilter->SetReplaceValue(2);
connectedThresFilter->ReleaseDataFlagOn();
connectedThresFilter->ReleaseDataBeforeUpdateFlagOn();
typedef itk::BinaryThresholdImageFilter<CharImageType, CharImageType> BinaryThresholdFilterType;
BinaryThresholdFilterType::Pointer binThresFilter = BinaryThresholdFilterType::New();
binThresFilter->SetInput(connectedThresFilter->GetOutput());
binThresFilter->SetLowerThreshold(0);
binThresFilter->SetUpperThreshold(0);
binThresFilter->SetInsideValue(50);
binThresFilter->SetOutsideValue(0);
binThresFilter->ReleaseDataFlagOn();
binThresFilter->ReleaseDataBeforeUpdateFlagOn();
typedef itk::AddImageFilter<CharImageType, CharImageType, CharImageType> AddFilterType;
AddFilterType::Pointer addFilter = AddFilterType::New();
addFilter->SetInput1(blurredImage);
addFilter->SetInput2(binThresFilter->GetOutput());
addFilter->ReleaseDataFlagOn();
addFilter->ReleaseDataBeforeUpdateFlagOn();
addFilter->Update();
ProgressBar::GetInstance()->Progress(1);
// Surface extraction
MITK_INFO << "Surface extraction...";
Image::Pointer filteredImage = Image::New();
CastToMitkImage(addFilter->GetOutput(), filteredImage);
filteredImage->SetGeometry(geometry);
ImageToSurfaceFilter::Pointer imageToSurfaceFilter = ImageToSurfaceFilter::New();
imageToSurfaceFilter->SetInput(filteredImage);
imageToSurfaceFilter->SetThreshold(50);
imageToSurfaceFilter->SmoothOn();
imageToSurfaceFilter->SetDecimate(ImageToSurfaceFilter::NoDecimation);
m_Surface = imageToSurfaceFilter->GetOutput(0);
ProgressBar::GetInstance()->Progress(1);
// Mesh decimation
if (decimation > 0.0f && decimation < 1.0f)
{
MITK_INFO << "Quadric mesh decimation...";
vtkQuadricDecimation *quadricDecimation = vtkQuadricDecimation::New();
quadricDecimation->SetInputData(m_Surface->GetVtkPolyData());
quadricDecimation->SetTargetReduction(decimation);
quadricDecimation->AttributeErrorMetricOn();
quadricDecimation->GlobalWarningDisplayOff();
quadricDecimation->Update();
vtkCleanPolyData* cleaner = vtkCleanPolyData::New();
cleaner->SetInputConnection(quadricDecimation->GetOutputPort());
cleaner->PieceInvariantOn();
cleaner->ConvertLinesToPointsOn();
cleaner->ConvertStripsToPolysOn();
cleaner->PointMergingOn();
cleaner->Update();
m_Surface->SetVtkPolyData(cleaner->GetOutput());
}
ProgressBar::GetInstance()->Progress(1);
// Compute Normals
vtkPolyDataNormals* computeNormals = vtkPolyDataNormals::New();
computeNormals->SetInputData(m_Surface->GetVtkPolyData());
computeNormals->SetFeatureAngle(360.0f);
computeNormals->FlipNormalsOff();
computeNormals->Update();
m_Surface->SetVtkPolyData(computeNormals->GetOutput());
return true;
}
// perform B-spline registration for 2D image
void runBspline2D(StringVector& args) {
typedef itk::BSplineTransform<double, 2, 3> TransformType;
typedef itk::LBFGSOptimizer OptimizerType;
typedef itk::MeanSquaresImageToImageMetric<RealImage2, RealImage2> MetricType;
typedef itk:: LinearInterpolateImageFunction<RealImage2, double> InterpolatorType;
typedef itk::ImageRegistrationMethod<RealImage2, RealImage2> RegistrationType;
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
// The old registration framework has problems with multi-threading
// For now, we set the number of threads to 1
registration->SetNumberOfThreads(1);
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
registration->SetInterpolator( interpolator );
TransformType::Pointer transform = TransformType::New();
registration->SetTransform( transform );
ImageIO<RealImage2> io;
// Create the synthetic images
RealImage2::Pointer fixedImage = io.ReadImage(args[0]);
RealImage2::Pointer movingImage = io.ReadImage(args[1]);
// Setup the registration
registration->SetFixedImage( fixedImage );
registration->SetMovingImage( movingImage);
RealImage2::RegionType fixedRegion = fixedImage->GetBufferedRegion();
registration->SetFixedImageRegion( fixedRegion );
TransformType::PhysicalDimensionsType fixedPhysicalDimensions;
TransformType::MeshSizeType meshSize;
for( unsigned int i=0; i < 2; i++ )
{
fixedPhysicalDimensions[i] = fixedImage->GetSpacing()[i] *
static_cast<double>(
fixedImage->GetLargestPossibleRegion().GetSize()[i] - 1 );
}
unsigned int numberOfGridNodesInOneDimension = 18;
meshSize.Fill( numberOfGridNodesInOneDimension - 3 );
transform->SetTransformDomainOrigin( fixedImage->GetOrigin() );
transform->SetTransformDomainPhysicalDimensions( fixedPhysicalDimensions );
transform->SetTransformDomainMeshSize( meshSize );
transform->SetTransformDomainDirection( fixedImage->GetDirection() );
typedef TransformType::ParametersType ParametersType;
const unsigned int numberOfParameters =
transform->GetNumberOfParameters();
ParametersType parameters( numberOfParameters );
parameters.Fill( 0.0 );
transform->SetParameters( parameters );
// We now pass the parameters of the current transform as the initial
// parameters to be used when the registration process starts.
registration->SetInitialTransformParameters( transform->GetParameters() );
std::cout << "Intial Parameters = " << std::endl;
std::cout << transform->GetParameters() << std::endl;
// Next we set the parameters of the LBFGS Optimizer.
optimizer->SetGradientConvergenceTolerance( 0.005 );
optimizer->SetLineSearchAccuracy( 0.9 );
optimizer->SetDefaultStepLength( .1 );
optimizer->TraceOn();
optimizer->SetMaximumNumberOfFunctionEvaluations( 1000 );
std::cout << std::endl << "Starting Registration" << std::endl;
try
{
registration->Update();
std::cout << "Optimizer stop condition = "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return;
}
OptimizerType::ParametersType finalParameters =
registration->GetLastTransformParameters();
std::cout << "Last Transform Parameters" << std::endl;
std::cout << finalParameters << std::endl;
transform->SetParameters( finalParameters );
typedef itk::ResampleImageFilter<RealImage2, RealImage2> ResampleFilterType;
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform( transform );
resample->SetInput( movingImage );
resample->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
resample->SetOutputOrigin( fixedImage->GetOrigin() );
resample->SetOutputSpacing( fixedImage->GetSpacing() );
resample->SetOutputDirection( fixedImage->GetDirection() );
resample->SetDefaultPixelValue( 100 );
resample->Update();
io.WriteImage(args[2], resample->GetOutput());
}
/**
* @brief 3D resample data to new grid size
*
* @param M Incoming data
* @param f Resampling factor in all 3 dimensions
* @param im Interpolation method (LINEAR|BSPLINE)
*
* @return Resampled data
*/
template<class T> static Matrix<T>
resample (const Matrix<T>& M, const Matrix<double>& f, const InterpMethod& im) {
Matrix <T> res = M;
#ifdef HAVE_INSIGHT
typedef typename itk::OrientedImage< T, 3 > InputImageType;
typedef typename itk::OrientedImage< T, 3 > OutputImageType;
typedef typename itk::IdentityTransform< double, 3 > TransformType;
typedef typename itk::LinearInterpolateImageFunction< InputImageType, double > InterpolatorType;
typedef typename itk::ResampleImageFilter< InputImageType, InputImageType > ResampleFilterType;
typename InterpolatorType::Pointer linterp = InterpolatorType::New();
TransformType::Pointer trafo = TransformType::New();
trafo->SetIdentity();
typename InputImageType::SpacingType space;
space[0] = 1.0/f[0];
space[1] = 1.0/f[1];
space[2] = 1.0/f[2];
typedef typename InputImageType::SizeType::SizeValueType SizeValueType;
typename InputImageType::SizeType size;
size[0] = static_cast<SizeValueType>(res.Dim(0));
size[1] = static_cast<SizeValueType>(res.Dim(1));
size[2] = static_cast<SizeValueType>(res.Dim(2));
typename itk::OrientedImage< T, 3 >::Pointer input = itk::OrientedImage< T, 3 >::New();
typename itk::OrientedImage< T, 3 >::Pointer output = itk::OrientedImage< T, 3 >::New();
typename itk::Image< T, 3 >::IndexType ipos;
ipos[0] = 0; ipos[1] = 0; ipos[2] = 0;
typename itk::Image< T, 3 >::IndexType opos;
opos[0] = 0; opos[1] = 0; opos[2] = 0;
typename itk::Image< T, 3 >::RegionType ireg;
ireg.SetSize(size);
ireg.SetIndex(ipos);
input->SetRegions(ireg);
input->Allocate();
typename itk::Image< T, 3 >::RegionType oreg;
oreg.SetSize(size);
ireg.SetIndex(opos);
output->SetRegions(oreg);
output->Allocate();
for (size_t z = 0; z < res.Dim(2); z++)
for (size_t y = 0; y < res.Dim(1); y++)
for (size_t x = 0; x < res.Dim(0); x++) {
ipos[0] = x; ipos[1] = y; ipos[2] = z;
input->SetPixel (ipos, res.At(x,y,z));
}
typename ResampleFilterType::Pointer rs = ResampleFilterType::New();
rs->SetInput( input );
rs->SetTransform( trafo );
rs->SetInterpolator( linterp );
rs->SetOutputOrigin ( input->GetOrigin());
rs->SetOutputSpacing ( space );
rs->SetOutputDirection ( input->GetDirection());
rs->SetSize ( size );
rs->Update ();
output = rs->GetOutput();
res = Matrix<T> (res.Dim(0)*f[0], res.Dim(1)*f[1], res.Dim(2)*f[2]);
res.Res(0) = res.Res(0)/f[0];
res.Res(1) = res.Dim(1)/f[1];
res.Res(2) = res.Dim(2)/f[2];
for (size_t z = 0; z < res.Dim(2); z++)
for (size_t y = 0; y < res.Dim(1); y++)
for (size_t x = 0; x < res.Dim(0); x++) {
opos[0] = x; opos[1] = y; opos[2] = z;
res.At(x,y,z) = output->GetPixel (opos);
}
#else
printf ("ITK ERROR - Resampling not performed without ITK!\n");
#endif
return res;
}
int main (int argc, char **argv)
{
int verbose=0, clobber=0,skip_grid=0;
int order=2;
std::string like_f,xfm_f,output_f,input_f;
double uniformize=0.0;
int invert=0;
char *history = time_stamp(argc, argv);
static struct option long_options[] = {
{"verbose", no_argument, &verbose, 1},
{"quiet", no_argument, &verbose, 0},
{"clobber", no_argument, &clobber, 1},
{"like", required_argument, 0, 'l'},
{"transform", required_argument, 0, 't'},
{"order", required_argument, 0, 'o'},
{"uniformize", required_argument, 0, 'u'},
{"invert_transform", no_argument, &invert, 1},
{0, 0, 0, 0}
};
for (;;) {
/* getopt_long stores the option index here. */
int option_index = 0;
int c = getopt_long (argc, argv, "vqcl:t:o:u:", long_options, &option_index);
/* Detect the end of the options. */
if (c == -1) break;
switch (c)
{
case 0:
break;
case 'v':
cout << "Version: 0.1" << endl;
return 0;
case 'l':
like_f=optarg; break;
case 't':
xfm_f=optarg; break;
case 'o':
order=atoi(optarg);break;
case 'u':
uniformize=atof(optarg);break;
case '?':
/* getopt_long already printed an error message. */
default:
show_usage (argv[0]);
return 1;
}
}
if ((argc - optind) < 2) {
show_usage(argv[0]);
return 1;
}
input_f=argv[optind];
output_f=argv[optind+1];
if (!clobber && !access (output_f.c_str (), F_OK))
{
std::cerr << output_f.c_str () << " Exists!" << std::endl;
return 1;
}
try
{
itk::ObjectFactoryBase::RegisterFactory(itk::MincImageIOFactory::New());
itk::ImageFileReader<minc::image3d >::Pointer reader = itk::ImageFileReader<minc::image3d >::New();
//initializing the reader
reader->SetFileName(input_f.c_str());
reader->Update();
minc::image3d::Pointer in=reader->GetOutput();
FilterType::Pointer filter = FilterType::New();
//creating coordinate transformation objects
TransformType::Pointer transform = TransformType::New();
if(!xfm_f.empty())
{
//reading a minc style xfm file
transform->OpenXfm(xfm_f.c_str());
if(!invert) transform->Invert(); //should be inverted by default to walk through target space
filter->SetTransform( transform );
}
//creating the interpolator
InterpolatorType::Pointer interpolator = InterpolatorType::New();
interpolator->SetSplineOrder(order);
filter->SetInterpolator( interpolator );
filter->SetDefaultPixelValue( 0 );
//this is for processing using batch system
filter->SetNumberOfThreads(1);
if(!like_f.empty())
{
itk::ImageFileReader<minc::image3d >::Pointer reader = itk::ImageFileReader<minc::image3d >::New();
reader->SetFileName(like_f.c_str());
reader->Update();
if(uniformize!=0.0)
{
generate_uniform_sampling(filter,reader->GetOutput(),uniformize);
} else {
filter->SetOutputParametersFromImage(reader->GetOutput());
filter->SetOutputDirection(reader->GetOutput()->GetDirection());
}
}
else
{
if(uniformize!=0.0)
{
generate_uniform_sampling(filter,in,uniformize);
} else {
//we are using original sampling
filter->SetOutputParametersFromImage(in);
filter->SetOutputDirection(in->GetDirection());
}
}
filter->SetInput(in);
filter->Update();
//copy the metadate information, for some reason it is not preserved
//filter->GetOutput()->SetMetaDataDictionary(reader->GetOutput()->GetMetaDataDictionary());
minc::image3d::Pointer out=filter->GetOutput();
minc::copy_metadata(out,in);
minc::append_history(out,history);
free(history);
//generic file writer
itk::ImageFileWriter< minc::image3d >::Pointer writer = itk::ImageFileWriter<minc::image3d >::New();
writer->SetFileName(output_f.c_str());
writer->SetInput( out );
//writer->UseInputMetaDataDictionaryOn();
writer->Update();
return 0;
} catch (const minc::generic_error & err) {
cerr << "Got an error at:" << err.file () << ":" << err.line () << endl;
return 1;
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return 2;
}
return 0;
};
int main( int argc, char *argv[] )
{
string input_name;
string output_dir;
if (argc == 3) {
input_name = argv[1];
output_dir = argv[2];
}
const unsigned int Dimension = 3;
const unsigned int OutDimension = 2;
typedef short InputPixelType;
typedef int FilterPixelType;
typedef itk::Image< InputPixelType, Dimension > InputImageType;
typedef itk::Image< FilterPixelType, Dimension > FilterImageType;
typedef itk::Image< FilterPixelType, OutDimension > OutFilterImageType;
InputImageType::Pointer image;
itk::MetaDataDictionary dict;
if (input_name.size() && output_dir.size())
{
if (boost::filesystem::is_regular_file( input_name )) {
typedef itk::ImageFileReader< InputImageType > ReaderType;
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName( input_name );
try
{
reader->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ERROR: ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
image = reader->GetOutput();
dict = reader->GetMetaDataDictionary();
} else if (boost::filesystem::is_directory( input_name )) {
itkBasic::SeriesReader sreader( input_name );
sreader.readSeriesData( 2 );
try
{
itkBasic::ReaderType::Pointer imageReader = itkBasic::ReaderType::New();
itkBasic::FileNamesContainer fc;
sreader.getSeriesFileNames(0, fc);
image = itkBasic::getDicomSerie( fc, imageReader, 1 );
dict = *((*imageReader->GetMetaDataDictionaryArray())[0]);
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ERROR: ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
}
}
if (!image) {
std::cerr << argv[0] << ": input output" << std::endl;
exit(1);
}
typedef itk::SigmoidImageFilter< InputImageType, FilterImageType > SigmoidCasterType;
SigmoidCasterType::Pointer sigmoidcaster = SigmoidCasterType::New();
sigmoidcaster->SetInput( image );
sigmoidcaster->SetOutputMaximum( 4000 );
sigmoidcaster->SetOutputMinimum( 1000 );
typedef itk::AccumulateImageFilter< FilterImageType, FilterImageType > AccumulateFilter;
AccumulateFilter::Pointer accumulator = AccumulateFilter::New();
accumulator->SetAccumulateDimension(1);
accumulator->SetInput( sigmoidcaster->GetOutput() );
typedef itk::ExtractImageFilter< FilterImageType, OutFilterImageType > ExtractFilter;
ExtractFilter::Pointer extractor = ExtractFilter::New();
extractor->SetInput( accumulator->GetOutput() );
FilterImageType::Pointer accuOut = accumulator->GetOutput();
accuOut->UpdateOutputInformation();
FilterImageType::RegionType extractRegion = accuOut->GetLargestPossibleRegion();
extractRegion.SetSize(1,0);
extractor->SetExtractionRegion( extractRegion );
typedef itk::ResampleImageFilter<OutFilterImageType, OutFilterImageType > ResampleFilter;
ResampleFilter::Pointer resampler = ResampleFilter::New();
resampler->SetInput( extractor->GetOutput() );
typedef itk::BSplineInterpolateImageFunction< OutFilterImageType > InterpolatorType;
InterpolatorType::Pointer interpolator = InterpolatorType::New();
interpolator->SetSplineOrder(3);
resampler->SetInterpolator( interpolator );
OutFilterImageType::Pointer exOut = extractor->GetOutput();
exOut->UpdateOutputInformation();
typedef itk::CenteredRigid2DTransform< double > TransformType;
TransformType::Pointer transform = TransformType::New();
transform->SetIdentity();
OutFilterImageType::PointType exOutCenter = exOut->GetOrigin();
exOutCenter[0] += (exOut->GetLargestPossibleRegion().GetSize()[0]-1) * exOut->GetSpacing()[0] *.5;
exOutCenter[1] += (exOut->GetLargestPossibleRegion().GetSize()[1]-1) * exOut->GetSpacing()[1] *.5;
transform->SetCenter( exOutCenter );
transform->SetAngleInDegrees( 180 );
resampler->SetTransform( transform );
resampler->SetOutputParametersFromImage( exOut );
OutFilterImageType::SpacingType resampleSpacing = exOut->GetSpacing();
resampleSpacing.Fill( std::min( resampleSpacing[0], resampleSpacing[1] ) );
OutFilterImageType::SizeType resampleSize;
resampleSize[0] = exOut->GetLargestPossibleRegion().GetSize()[0] * exOut->GetSpacing()[0] / resampleSpacing[0];
resampleSize[1] = exOut->GetLargestPossibleRegion().GetSize()[1] * exOut->GetSpacing()[1] / resampleSpacing[1];
resampler->SetSize( resampleSize );
resampler->SetOutputSpacing( resampleSpacing );
OutFilterImageType::Pointer result = resampler->GetOutput();
sigmoidcaster->SetBeta( -500 );
sigmoidcaster->SetAlpha( 5 );
result->Update();
int outDicomIndex = 0;
itk::EncapsulateMetaData( dict, "0008|0008", string("DERIVED\\SECONDARY\\AXIAL"));
boost::filesystem::path outpath = output_dir;
outpath = outpath / "IM%06d";
std::vector< itk::MetaDataDictionary* > dictArray;
dictArray.push_back(&dict);
itkBasic::writeDicomSeries( itkBasic::ImageRescale(itkBasic::ImageSharp(result, 0.5), -1000, 4000), outpath.string(), &dictArray, outDicomIndex);
// itkBasic::ImageSave( itkBasic::ImageSharp(result, 0.5), boost::str( boost::format("%s.%s.png") % output_name % "lung" ), 1, 0); // Auto Level
sigmoidcaster->SetBeta( 1000 );
sigmoidcaster->SetAlpha( 300 );
result->Update();
itkBasic::writeDicomSeries( itkBasic::ImageRescale(itkBasic::ImageSharp(result, 0.5), -1000, 4000), outpath.string(), &dictArray, outDicomIndex);
// itkBasic::ImageSave( itkBasic::ImageSharp(result, 0.5), boost::str( boost::format("%s.%s.png") % output_name % "bone" ), 1, 0); // Auto Level
sigmoidcaster->SetBeta( 0 );
sigmoidcaster->SetAlpha( 2000 );
result->Update();
itkBasic::writeDicomSeries( itkBasic::ImageRescale(itkBasic::ImageSharp(result, 0.5), -1000, 4000), outpath.string(), &dictArray, outDicomIndex);
// itkBasic::ImageSave( itkBasic::ImageSharp(result, 0.5), boost::str( boost::format("%s.%s.png") % output_name % "normal" ), 1, 0); // Auto Level
}
int main(int argc, char *argv[])
{
std::string inputFilenamesFilename = argv[1];
double keyPointIntensityThreshold = atof(argv[2]);
double dogSplitsPerOctave = atof(argv[3]);
double statingScale = atof(argv[4]);
double eLocation = atof(argv[5]);
double eScale = std::log(atof(argv[6]));
double eOrientation = atof(argv[7]);
double gammaValue = atof(argv[8]);
std::string distanceMapFilenamesFilename = argv[9];
double extractionDistanceThreshold = atof(argv[10]);
// load up the set of aligned images
FilenamesReader::FilenamesType inputFilenames = FilenamesReader::Read(inputFilenamesFilename);
FilenamesReader::FilenamesType distanceMapFilenames = FilenamesReader::Read(distanceMapFilenamesFilename);
ImageVolumeList images;
RealVolumeList distanceMaps;
for(unsigned int i = 0; i < inputFilenames.size(); i++)
{
ImageVolume::Pointer image = ImageVolumeIO::Read(inputFilenames[i]);
images.push_back(image);
RealVolume::Pointer distMap = RealVolumeIO::Read(distanceMapFilenames[i]);
distanceMaps.push_back(distMap);
}
unsigned int sliceToTest = 7;
// for each slice we want to learn the features
const unsigned int sliceNum = images.front()->GetLargestPossibleRegion().GetSize()[2];
for(unsigned int slice = sliceToTest; slice < sliceNum; slice++)
{
// get the set of slices that have some image data in them
ImageSliceList validImages;
RealSliceList validDistanceMaps;
for(unsigned int im = 0; im < images.size(); im++)
{
ImageSlice::Pointer extractedSlice = ImageSlice::New();
RealSlice::Pointer distanceSlice = RealSlice::New();
ExtractSlice<ImageVolume, ImageSlice>(images[im], slice, extractedSlice);
ExtractSlice<RealVolume, RealSlice>(distanceMaps[im], slice, distanceSlice);
if(ImageContainsData(extractedSlice))
{
validDistanceMaps.push_back(distanceSlice);
validImages.push_back(extractedSlice);
}
}
/*
if(validImages.size() < 3)
continue;
*/
std::cout << "Slice Num: " << slice << " Image Number: " << validImages.size() << std::endl;
typedef itk::Vector<double, 2> VectorType;
typedef itk::Image<VectorType, 2> GradientType;
typedef filter::HistogramOfGradeintsFeatureExtractor<GradientType> FeatureBuilderType;
typedef FeatureBuilderType::FeatureType HoGFeatureType;
std::vector<HoGFeatureType> allFeatures;
std::vector<HoGFeatureType> allFeatures1;
std::vector<HoGFeatureType> allFeatures2;
unsigned int featureCount = 0;
for(unsigned int im = 0; im < validImages.size(); im++)
{
ImageSlice::Pointer extractedSlice = validImages[im];
// first we extract all of the keypoints points
typedef filter::DoGKeyPointExtractor<utils::ImageSlice> ExtractorType;
ExtractorType::Pointer extractor = ExtractorType::New();
extractor->SetInput(extractedSlice);
extractor->SetKeypointThreshold(keyPointIntensityThreshold);
extractor->SetSplitsPerOctave(dogSplitsPerOctave);
extractor->SetStartingSigma(statingScale);
extractor->SetDistanceMap(validDistanceMaps[im]);
extractor->SetDistanceThreshold(extractionDistanceThreshold);
extractor->Update();
// orientate the feature points
typedef filter::KeyPointOrientator<utils::ImageSlice> Orientator;
Orientator::Pointer orientator = Orientator::New();
orientator->SetInput(extractedSlice);
orientator->SetKeyPoints(extractor->GetOutput());
orientator->SetHistogramBins(32);
orientator->SetSigmaScale(2);
orientator->SetSampleRadius(5);
orientator->Update();
Orientator::OrientatedKeyPointMap orientateKeyPoints = orientator->GetOutput();
// now we go through the features and compute the HOG descriptors
Orientator::OrientatedKeyPointMap::iterator keyPointIt = orientateKeyPoints.begin();
std::cout << orientateKeyPoints.size() << std::endl;
while(keyPointIt != orientateKeyPoints.end())
{
double sigma = keyPointIt->first;
Orientator::OrientatedKeyPointList keyPoints = keyPointIt->second;
// smooth the image to the sigma level
typedef itk::DiscreteGaussianImageFilter<utils::ImageSlice, utils::RealSlice> Smoother;
Smoother::Pointer smoother = Smoother::New();
smoother->SetInput(extractedSlice);
smoother->SetVariance(sigma*sigma);
smoother->SetUseImageSpacingOn();
typedef itk::GradientRecursiveGaussianImageFilter<RealSlice, GradientType> GradientFilterType;
GradientFilterType::Pointer gradientFilter = GradientFilterType::New();
gradientFilter->SetInput(smoother->GetOutput());
//gradientFilter->SetSigma(sigma);
gradientFilter->Update();
std::cout << "Doing Sigma " << sigma << " Key Point Number: " << keyPoints.size() << std::endl;
for(unsigned int fnum = 0; fnum < keyPoints.size(); fnum++)
{
Orientator::OrientatedKeyPoint keyPoint = keyPoints[fnum];
// build the tranform
typedef itk::CenteredRigid2DTransform<double> TransformType;
TransformType::Pointer transform = TransformType::New();
transform->SetCenter(keyPoint.location);
transform->SetAngleInDegrees(360-keyPoint.degrees);
// extract the patch from the gradient image
typedef filter::ImagePatchExtractor<GradientType> PatchExtractorType;
PatchExtractorType::Pointer patchExtractor = PatchExtractorType::New();
patchExtractor->SetInput(gradientFilter->GetOutput());
patchExtractor->SetTransform(transform);
patchExtractor->SetScale(keyPoint.scale*2);
PatchExtractorType::SizeType patchSize;
patchSize.Fill(10);
patchExtractor->SetPatchSize(patchSize);
patchExtractor->SetInputPoint(keyPoint.location);
patchExtractor->Update();
/*
// validate the keypoint
typedef filter::StructureTensorKeyPointValidator<utils::ImageSlice> ValidatorType;
ValidatorType::Pointer validator = ValidatorType::New();
validator->SetInput(extractedSlice);
validator->SetKeyPoint(keyPoint);
validator->SetRatio(validatorBeta);
validator->Update();
bool valid = validator->IsValid();
*/
// create the descriptor
FeatureBuilderType::Pointer builder = FeatureBuilderType::New();
builder->SetInput(patchExtractor->GetOutput());
builder->SetOrientationBins(8);
builder->SetKeyPoint(keyPoint);
builder->Update();
// add the feature to the list
FeatureBuilderType::FeatureType feature = builder->GetOutput();
feature.featureId = featureCount;
allFeatures.push_back(feature);
featureCount++;
}
keyPointIt++;
}
}
std::cout << "Computing Distance Matrix" << std::endl;
// compute the distance matrix
typedef utils::DoubleMatrixType MatrixType;
MatrixType distanceMatrix = MatrixType::Zero(allFeatures.size(), allFeatures.size());
ComputeDifferenceMatrix(allFeatures, distanceMatrix);
std::cout << "Grouping Features" << std::endl;
// now we group the features by thier geometry
typedef filter::FeaturePointGrouper<2> GrouperType;
GrouperType::Pointer grouper = GrouperType::New();
grouper->SetInput(allFeatures);
grouper->SetAngleThreshold(eOrientation);
grouper->SetLocationThreshold(eLocation);
grouper->SetScaleThreshold(eScale);
grouper->Update();
std::cout << "Creating Clusters" << std::endl;
GrouperType::FeatureGroupList clusters = grouper->GetOutput();
std::sort(clusters.begin(), clusters.end());
GrouperType::FeatureGroupList newClusters;
for(unsigned int i = 0; i < clusters.size(); i++)
{
typedef filter::FeatureClusterLearner<2> ClusterLearnerType;
ClusterLearnerType::Pointer learner = ClusterLearnerType::New();
learner->SetInput(clusters[i]);
learner->SetFeatures(allFeatures);
learner->SetDistanceMatrix(distanceMatrix);
learner->SetGamma(gammaValue);
learner->Update();
ClusterLearnerType::ClusterType newCluster = learner->GetOutput();
newClusters.push_back(newCluster);
}
std::cout << "Culling Clusters" << std::endl;
typedef filter::FeatureClusterCuller<2> Culler;
Culler::Pointer culler = Culler::New();
culler->SetInput(newClusters);
culler->Update();
Culler::ClusterList culledClusters = culler->GetOutput();
std::sort(culledClusters.begin(), culledClusters.end());
for(unsigned int i = 0; i < culledClusters.size(); i++)
{
typedef filter::ClusterDistributionGenerator<2> DistributionGenerator;
DistributionGenerator::Pointer generator = DistributionGenerator::New();
generator->SetInput(culledClusters[i]);
generator->Update();
exit(1);
}
/*
ImageSlice::Pointer extractedSlice = ImageSlice::New();
ExtractSlice<ImageVolume, ImageSlice>(images[0], sliceToTest, extractedSlice);
std::vector<std::pair<int, ImageSlice::PointType> > testOut;
for(unsigned int i = 0; i < culledClusters.size(); i++)
{
for(unsigned int j = 0; j < culledClusters[i].clusterMembers.size(); j++)
{
std::pair<int, ImageSlice::PointType> p(i, culledClusters[i].clusterMembers[j].keyPoint.location);
testOut.push_back(p);
}
}
utils::DoubleMatrixType pOut = utils::DoubleMatrixType::Zero(testOut.size(), 2);
utils::IntMatrixType iOut = utils::IntMatrixType::Zero(testOut.size(),1);
for(unsigned int i = 0; i < testOut.size(); i++)
{
itk::ContinuousIndex<double, 2> contIndex;
extractedSlice->TransformPhysicalPointToContinuousIndex(testOut[i].second, contIndex);
pOut(i,0) = contIndex[0];
pOut(i,1) = contIndex[1];
iOut(i,0) = testOut[i].first;
}
utils::MatrixDataSet::Pointer dout = utils::MatrixDataSet::New();
dout->AddData("locations", pOut);
dout->AddData("index", iOut);
utils::MatrixWriter::Pointer writer = utils::MatrixWriter::New();
writer->SetInput(dout);
writer->SetFilename("data.hdf5");
writer->Update();
exit(1);
*/
/*
// compute the affinity matrix between all of the features
unsigned int numFeatures = allFeatures.size();
double sum = 0.0;
int count = 0;
int max = 0;
int maxId = 0;
std::vector<int> counts;
std::vector<Cluster> allGroups;
// groupd all the features that have a similar location / scale / orientation
for(unsigned int i = 0; i < numFeatures; i++)
{
Cluster cluster;
cluster.featureIndex = i;
cluster.feature = allFeatures[i];
cluster.e = 0.0;
cluster.members.push_back(allFeatures[i]);
for(unsigned int j = 0; j < numFeatures; j++)
{
if(i == j) continue;
if(AreSimilar(allFeatures[i], allFeatures[j],
eLocation, eOrientation, eScale))
{
cluster.members.push_back(allFeatures[j]);
}
}
counts.push_back(cluster.members.size());
if(cluster.members.size() > max)
{
max = cluster.members.size();
maxId = i;
}
allGroups.push_back(cluster);
sum += cluster.members.size();
std::sort(cluster.members.begin(), cluster.members.end(), member_sort);
count++;
}
std::sort(counts.begin(), counts.end());
for(unsigned int i = 0; i < counts.size(); i++)
{
std::cout << counts[i] << std::endl;
}
// compute the difference matrix
utils::DoubleMatrixType diffMat;
ComputeDifferenceMatrix(allFeatures, diffMat);
// loop through the groups to form the clusters
std::vector<Cluster> allClusters;
for(unsigned int i = 0; i < allGroups.size(); i++)
{
Cluster cluster;
CreateCluster(allGroups[i], allFeatures, diffMat, cluster);
allClusters.push_back(cluster);
}
// remove duplicates
std::vector<int> toRemove;
for(unsigned int i = 0; i < allClusters.size(); i++)
{
bool duplicate = false;
for(unsigned int j = i; j < allClusters.size(); j++)
{
if(i == j) continue;
if(allClusters[i].members.size() != allClusters[j].members.size())
continue;
bool sameMembers = true;
for(unsigned int k = 0; k < allClusters[i].members.size(); k++)
{
if(allClusters[i].members[k].index != allClusters[j].members[k].index)
{
sameMembers = false;
break;
}
}
if(sameMembers)
{
duplicate = true;
}
if(duplicate) break;
}
if(duplicate) toRemove.push_back(i);
}
std::cout << allClusters.size() << std::endl;
for(unsigned int i = 0; i < toRemove.size(); i++)
{
// allClusters.erase(allGroups.begin()+(toRemove[i]-i));
}
std::cout << allClusters.size() << std::endl;
// trim the clusters
std::vector<Cluster> trimmedClusters;
TrimClusters(allClusters, trimmedClusters);
std::cout << trimmedClusters.size() << std::endl;
std::vector<std::pair<int, ImageSlice::PointType> > testOut;
for(unsigned int i = 0; i < trimmedClusters.size(); i++)
{
for(unsigned int j = 0; j < trimmedClusters[i].members.size(); j++)
{
std::pair<int, ImageSlice::PointType> p(i, trimmedClusters[i].members[j].location);
testOut.push_back(p);
}
std::cout << trimmedClusters[i].members.size() << std::endl;
}
ImageSlice::Pointer extractedSlice = ImageSlice::New();
ExtractSlice<ImageVolume, ImageSlice>(images[0], sliceToTest, extractedSlice);
utils::DoubleMatrixType pOut = utils::DoubleMatrixType::Zero(testOut.size(), 2);
utils::IntMatrixType iOut = utils::IntMatrixType::Zero(testOut.size(),1);
for(unsigned int i = 0; i < testOut.size(); i++)
{
itk::ContinuousIndex<double, 2> contIndex;
extractedSlice->TransformPhysicalPointToContinuousIndex(testOut[i].second, contIndex);
pOut(i,0) = contIndex[0];
pOut(i,1) = contIndex[1];
// compute the image indexes
iOut(i,0) = testOut[i].first;
}
utils::MatrixDataSet::Pointer dout = utils::MatrixDataSet::New();
dout->AddData("locations", pOut);
dout->AddData("index", iOut);
utils::MatrixWriter::Pointer writer = utils::MatrixWriter::New();
writer->SetInput(dout);
writer->SetFilename("data.hdf5");
writer->Update();
exit(1);
*/
}
return 0;
}
int main( int argc, char *argv[] )
{
if( argc < 4 )
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " fixedImageFile movingImageFile ";
std::cerr << " outputImagefile [differenceBeforeRegistration] ";
std::cerr << " [differenceAfterRegistration] ";
std::cerr << " [sliceBeforeRegistration] ";
std::cerr << " [sliceDifferenceBeforeRegistration] ";
std::cerr << " [sliceDifferenceAfterRegistration] ";
std::cerr << " [sliceAfterRegistration] " << std::endl;
return EXIT_FAILURE;
}
const unsigned int Dimension = 3;
typedef float PixelType;
typedef itk::Image< PixelType, Dimension > FixedImageType;
typedef itk::Image< PixelType, Dimension > MovingImageType;
// Software Guide : BeginLatex
//
// The Transform class is instantiated using the code below. The only
// template parameter to this class is the representation type of the
// space coordinates.
//
// \index{itk::Versor\-Rigid3D\-Transform!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
typedef itk:: LinearInterpolateImageFunction< MovingImageType, double > InterpolatorType;
typedef itk::ImageRegistrationMethod< FixedImageType, MovingImageType > RegistrationType;
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
registration->SetInterpolator( interpolator );
// Software Guide : BeginLatex
//
// The transform object is constructed below and passed to the registration
// method.
//
// \index{itk::Versor\-Rigid3D\-Transform!New()}
// \index{itk::Versor\-Rigid3D\-Transform!Pointer}
// \index{itk::Registration\-Method!SetTransform()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = TransformType::New();
registration->SetTransform( transform );
// Software Guide : EndCodeSnippet
typedef itk::ImageFileReader< FixedImageType > FixedImageReaderType;
typedef itk::ImageFileReader< MovingImageType > MovingImageReaderType;
FixedImageReaderType::Pointer fixedImageReader = FixedImageReaderType::New();
MovingImageReaderType::Pointer movingImageReader = MovingImageReaderType::New();
fixedImageReader->SetFileName( argv[1] );
movingImageReader->SetFileName( argv[2] );
registration->SetFixedImage( fixedImageReader->GetOutput() );
registration->SetMovingImage( movingImageReader->GetOutput() );
fixedImageReader->Update();
registration->SetFixedImageRegion(
fixedImageReader->GetOutput()->GetBufferedRegion() );
// Software Guide : BeginLatex
//
// The input images are taken from readers. It is not necessary here to
// explicitly call \code{Update()} on the readers since the
// \doxygen{CenteredTransformInitializer} will do it as part of its
// computations. The following code instantiates the type of the
// initializer. This class is templated over the fixed and moving image type
// as well as the transform type. An initializer is then constructed by
// calling the \code{New()} method and assigning the result to a smart
// pointer.
//
// \index{itk::Centered\-Transform\-Initializer!Instantiation}
// \index{itk::Centered\-Transform\-Initializer!New()}
// \index{itk::Centered\-Transform\-Initializer!SmartPointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : BeginLatex
//
// Let's execute this example over some of the images available in the ftp
// site
//
// \url{ftp://public.kitware.com/pub/itk/Data/BrainWeb}
//
// Note that the images in the ftp site are compressed in \code{.tgz} files.
// You should download these files an uncompress them in your local system.
// After decompressing and extracting the files you could take a pair of
// volumes, for example the pair:
//
// \begin{itemize}
// \item \code{brainweb1e1a10f20.mha}
// \item \code{brainweb1e1a10f20Rot10Tx15.mha}
// \end{itemize}
//
// The second image is the result of intentionally rotating the first image
// by $10$ degrees around the origin and shifting it $15mm$ in $X$. The
// registration takes $24$ iterations and produces:
//
// \begin{center}
// \begin{verbatim}
// [-6.03744e-05, 5.91487e-06, -0.0871932, 2.64659, -17.4637, -0.00232496]
// \end{verbatim}
// \end{center}
//
// That are interpreted as
//
// \begin{itemize}
// \item Versor = $(-6.03744e-05, 5.91487e-06, -0.0871932)$
// \item Translation = $(2.64659, -17.4637, -0.00232496)$ millimeters
// \end{itemize}
//
// This Versor is equivalent to a rotation of $9.98$ degrees around the $Z$
// axis.
//
// Note that the reported translation is not the translation of $(15.0,0.0,0.0)$
// that we may be naively expecting. The reason is that the
// \code{VersorRigid3DTransform} is applying the rotation around the center
// found by the \code{CenteredTransformInitializer} and then adding the
// translation vector shown above.
//
// It is more illustrative in this case to take a look at the actual
// rotation matrix and offset resulting form the $6$ parameters.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
transform->SetParameters( finalParameters );
TransformType::MatrixType matrix = transform->GetMatrix();
TransformType::OffsetType offset = transform->GetOffset();
std::cout << "Matrix = " << std::endl << matrix << std::endl;
std::cout << "Offset = " << std::endl << offset << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The output of this print statements is
//
// \begin{center}
// \begin{verbatim}
// Matrix =
// 0.984795 0.173722 2.23132e-05
// -0.173722 0.984795 0.000119257
// -1.25621e-06 -0.00012132 1
//
// Offset =
// [-15.0105, -0.00672343, 0.0110854]
// \end{verbatim}
// \end{center}
//
// From the rotation matrix it is possible to deduce that the rotation is
// happening in the X,Y plane and that the angle is on the order of
// $\arcsin{(0.173722)}$ which is very close to 10 degrees, as we expected.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceBorder20}
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceR10X13Y17}
// \itkcaption[CenteredTransformInitializer input images]{Fixed and moving image
// provided as input to the registration method using
// CenteredTransformInitializer.}
// \label{fig:FixedMovingImageRegistration8}
// \end{figure}
//
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{ImageRegistration8Output}
// \includegraphics[width=0.32\textwidth]{ImageRegistration8DifferenceBefore}
// \includegraphics[width=0.32\textwidth]{ImageRegistration8DifferenceAfter}
// \itkcaption[CenteredTransformInitializer output images]{Resampled moving
// image (left). Differences between fixed and moving images, before (center)
// and after (right) registration with the
// CenteredTransformInitializer.}
// \label{fig:ImageRegistration8Outputs}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration8Outputs} shows the output of the
// registration. The center image in this figure shows the differences
// between the fixed image and the resampled moving image before the
// registration. The image on the right side presents the difference between
// the fixed image and the resampled moving image after the registration has
// been performed. Note that these images are individual slices extracted
// from the actual volumes. For details, look at the source code of this
// example, where the ExtractImageFilter is used to extract a slice from the
// the center of each one of the volumes. One of the main purposes of this
// example is to illustrate that the toolkit can perform registration on
// images of any dimension. The only limitations are, as usual, the amount of
// memory available for the images and the amount of computation time that it
// will take to complete the optimization process.
//
// \begin{figure}
// \center
// \includegraphics[height=0.32\textwidth]{ImageRegistration8TraceMetric}
// \includegraphics[height=0.32\textwidth]{ImageRegistration8TraceAngle}
// \includegraphics[height=0.32\textwidth]{ImageRegistration8TraceTranslations}
// \itkcaption[CenteredTransformInitializer output plots]{Plots of the metric,
// rotation angle, center of rotation and translations during the
// registration using CenteredTransformInitializer.}
// \label{fig:ImageRegistration8Plots}
// \end{figure}
//
// Figure \ref{fig:ImageRegistration8Plots} shows the plots of the main
// output parameters of the registration process. The metric values at every
// iteration. The Z component of the versor is plotted as an indication of
// how the rotation progress. The X,Y translation components of the
// registration are plotted at every iteration too.
//
// Shell and Gnuplot scripts for generating the diagrams in
// Figure~\ref{fig:ImageRegistration8Plots} are available in the directory
//
// \code{InsightDocuments/SoftwareGuide/Art}
//
// You are strongly encouraged to run the example code, since only in this
// way you can gain a first hand experience with the behavior of the
// registration process. Once again, this is a simple reflection of the
// philosophy that we put forward in this book:
//
// \emph{If you can not replicate it, then it does not exist!}.
//
// We have seen enough published papers with pretty pictures, presenting
// results that in practice are impossible to replicate. That is vanity, not
// science.
//
// Software Guide : EndLatex
typedef itk::ResampleImageFilter<
MovingImageType,
FixedImageType > ResampleFilterType;
TransformType::Pointer finalTransform = TransformType::New();
finalTransform->SetCenter( transform->GetCenter() );
finalTransform->SetParameters( finalParameters );
finalTransform->SetFixedParameters( transform->GetFixedParameters() );
ResampleFilterType::Pointer resampler = ResampleFilterType::New();
resampler->SetTransform( finalTransform );
resampler->SetInput( movingImageReader->GetOutput() );
FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
resampler->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
resampler->SetOutputOrigin( fixedImage->GetOrigin() );
resampler->SetOutputSpacing( fixedImage->GetSpacing() );
resampler->SetOutputDirection( fixedImage->GetDirection() );
resampler->SetDefaultPixelValue( 100 );
typedef unsigned char OutputPixelType;
typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
typedef itk::CastImageFilter< FixedImageType, OutputImageType > CastFilterType;
typedef itk::ImageFileWriter< OutputImageType > WriterType;
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
writer->SetFileName( argv[3] );
caster->SetInput( resampler->GetOutput() );
writer->SetInput( caster->GetOutput() );
writer->Update();
typedef itk::SubtractImageFilter<
FixedImageType,
FixedImageType,
FixedImageType > DifferenceFilterType;
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
typedef itk::RescaleIntensityImageFilter<
FixedImageType,
OutputImageType > RescalerType;
RescalerType::Pointer intensityRescaler = RescalerType::New();
intensityRescaler->SetInput( difference->GetOutput() );
intensityRescaler->SetOutputMinimum( 0 );
intensityRescaler->SetOutputMaximum( 255 );
difference->SetInput1( fixedImageReader->GetOutput() );
difference->SetInput2( resampler->GetOutput() );
resampler->SetDefaultPixelValue( 1 );
WriterType::Pointer writer2 = WriterType::New();
writer2->SetInput( intensityRescaler->GetOutput() );
// Compute the difference image between the
// fixed and resampled moving image.
if( argc > 5 )
{
writer2->SetFileName( argv[5] );
writer2->Update();
}
typedef itk::IdentityTransform< double, Dimension > IdentityTransformType;
IdentityTransformType::Pointer identity = IdentityTransformType::New();
// Compute the difference image between the
// fixed and moving image before registration.
if( argc > 4 )
{
resampler->SetTransform( identity );
writer2->SetFileName( argv[4] );
writer2->Update();
}
//
// Here we extract slices from the input volume, and the difference volumes
// produced before and after the registration. These slices are presented as
// figures in the Software Guide.
//
//
typedef itk::Image< OutputPixelType, 2 > OutputSliceType;
typedef itk::ExtractImageFilter<
OutputImageType,
OutputSliceType > ExtractFilterType;
ExtractFilterType::Pointer extractor = ExtractFilterType::New();
extractor->SetDirectionCollapseToSubmatrix();
extractor->InPlaceOn();
FixedImageType::RegionType inputRegion =
fixedImage->GetLargestPossibleRegion();
FixedImageType::SizeType size = inputRegion.GetSize();
FixedImageType::IndexType start = inputRegion.GetIndex();
// Select one slice as output
size[2] = 0;
start[2] = 90;
FixedImageType::RegionType desiredRegion;
desiredRegion.SetSize( size );
desiredRegion.SetIndex( start );
extractor->SetExtractionRegion( desiredRegion );
typedef itk::ImageFileWriter< OutputSliceType > SliceWriterType;
SliceWriterType::Pointer sliceWriter = SliceWriterType::New();
sliceWriter->SetInput( extractor->GetOutput() );
if( argc > 6 )
{
extractor->SetInput( caster->GetOutput() );
resampler->SetTransform( identity );
sliceWriter->SetFileName( argv[6] );
sliceWriter->Update();
}
if( argc > 7 )
{
extractor->SetInput( intensityRescaler->GetOutput() );
resampler->SetTransform( identity );
sliceWriter->SetFileName( argv[7] );
sliceWriter->Update();
}
if( argc > 8 )
{
resampler->SetTransform( finalTransform );
sliceWriter->SetFileName( argv[8] );
sliceWriter->Update();
}
if( argc > 9 )
{
extractor->SetInput( caster->GetOutput() );
resampler->SetTransform( finalTransform );
sliceWriter->SetFileName( argv[9] );
sliceWriter->Update();
}
return EXIT_SUCCESS;
}
bool mitk::NavigationDataLandmarkTransformFilter::FindCorrespondentLandmarks(LandmarkPointContainer& sources, const LandmarkPointContainer& targets) const
{
if (sources.size() < 6 || targets.size() < 6)
return false;
//throw std::invalid_argument("ICP correspondence finding needs at least 6 landmarks");
/* lots of type definitions */
typedef itk::PointSet<mitk::ScalarType, 3> PointSetType;
//typedef itk::BoundingBox<PointSetType::PointIdentifier, PointSetType::PointDimension> BoundingBoxType;
typedef itk::EuclideanDistancePointMetric< PointSetType, PointSetType> MetricType;
//typedef MetricType::TransformType TransformBaseType;
//typedef MetricType::TransformType::ParametersType ParametersType;
//typedef TransformBaseType::JacobianType JacobianType;
//typedef itk::Euler3DTransform< double > TransformType;
typedef itk::VersorRigid3DTransform< double > TransformType;
typedef TransformType ParametersType;
typedef itk::PointSetToPointSetRegistrationMethod< PointSetType, PointSetType > RegistrationType;
/* copy landmarks to itk pointsets for registration */
PointSetType::Pointer sourcePointSet = PointSetType::New();
unsigned int i = 0;
for (LandmarkPointContainer::const_iterator it = sources.begin(); it != sources.end(); ++it)
{
PointSetType::PointType doublePoint;
mitk::itk2vtk(*it, doublePoint); // copy mitk::ScalarType point into double point as workaround to ITK 3.10 bug
sourcePointSet->SetPoint(i++, doublePoint /**it*/);
}
i = 0;
PointSetType::Pointer targetPointSet = PointSetType::New();
for (LandmarkPointContainer::const_iterator it = targets.begin(); it != targets.end(); ++it)
{
PointSetType::PointType doublePoint;
mitk::itk2vtk(*it, doublePoint); // copy mitk::ScalarType point into double point as workaround to ITK 3.10 bug
targetPointSet->SetPoint(i++, doublePoint /**it*/);
}
/* get centroid and extends of our pointsets */
//BoundingBoxType::Pointer sourceBoundingBox = BoundingBoxType::New();
//sourceBoundingBox->SetPoints(sourcePointSet->GetPoints());
//sourceBoundingBox->ComputeBoundingBox();
//BoundingBoxType::Pointer targetBoundingBox = BoundingBoxType::New();
//targetBoundingBox->SetPoints(targetPointSet->GetPoints());
//targetBoundingBox->ComputeBoundingBox();
TransformType::Pointer transform = TransformType::New();
transform->SetIdentity();
//transform->SetTranslation(targetBoundingBox->GetCenter() - sourceBoundingBox->GetCenter());
itk::LevenbergMarquardtOptimizer::Pointer optimizer = itk::LevenbergMarquardtOptimizer::New();
optimizer->SetUseCostFunctionGradient(false);
RegistrationType::Pointer registration = RegistrationType::New();
// Scale the translation components of the Transform in the Optimizer
itk::LevenbergMarquardtOptimizer::ScalesType scales(transform->GetNumberOfParameters());
const double translationScale = 5000; //sqrtf(targetBoundingBox->GetDiagonalLength2()) * 1000; // dynamic range of translations
const double rotationScale = 1.0; // dynamic range of rotations
scales[0] = 1.0 / rotationScale;
scales[1] = 1.0 / rotationScale;
scales[2] = 1.0 / rotationScale;
scales[3] = 1.0 / translationScale;
scales[4] = 1.0 / translationScale;
scales[5] = 1.0 / translationScale;
//scales.Fill(0.01);
unsigned long numberOfIterations = 80000;
double gradientTolerance = 1e-10; // convergence criterion
double valueTolerance = 1e-10; // convergence criterion
double epsilonFunction = 1e-10; // convergence criterion
optimizer->SetScales( scales );
optimizer->SetNumberOfIterations( numberOfIterations );
optimizer->SetValueTolerance( valueTolerance );
optimizer->SetGradientTolerance( gradientTolerance );
optimizer->SetEpsilonFunction( epsilonFunction );
registration->SetInitialTransformParameters( transform->GetParameters() );
//------------------------------------------------------
// Connect all the components required for Registration
//------------------------------------------------------
MetricType::Pointer metric = MetricType::New();
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
registration->SetTransform( transform );
registration->SetFixedPointSet( targetPointSet );
registration->SetMovingPointSet( sourcePointSet );
try
{
//registration->StartRegistration();
registration->Update();
}
catch( itk::ExceptionObject & e )
{
MITK_INFO << "Exception caught during ICP optimization: " << e;
return false;
//throw e;
}
MITK_INFO << "ICP successful: Solution = " << transform->GetParameters() << std::endl;
MITK_INFO << "Metric value: " << metric->GetValue(transform->GetParameters());
/* find point correspondences */
//mitk::PointLocator::Pointer pointLocator = mitk::PointLocator::New(); // <<- use mitk::PointLocator instead of searching manually?
//pointLocator->SetPoints()
for (LandmarkPointContainer::const_iterator sourcesIt = sources.begin(); sourcesIt != sources.end(); ++sourcesIt)
{
}
//MetricType::MeasureType closestDistances = metric->GetValue(transform->GetParameters());
//unsigned int index = 0;
LandmarkPointContainer sortedSources;
for (LandmarkPointContainer::const_iterator targetsIt = targets.begin(); targetsIt != targets.end(); ++targetsIt)
{
double minDistance = itk::NumericTraits<double>::max();
LandmarkPointContainer::iterator minDistanceIterator = sources.end();
for (LandmarkPointContainer::iterator sourcesIt = sources.begin(); sourcesIt != sources.end(); ++sourcesIt)
{
TransformInitializerType::LandmarkPointType transformedSource = transform->TransformPoint(*sourcesIt);
double dist = targetsIt->EuclideanDistanceTo(transformedSource);
MITK_INFO << "target: " << *targetsIt << ", source: " << *sourcesIt << ", transformed source: " << transformedSource << ", dist: " << dist;
if (dist < minDistance )
{
minDistanceIterator = sourcesIt;
minDistance = dist;
}
}
if (minDistanceIterator == sources.end())
return false;
MITK_INFO << "minimum distance point is: " << *minDistanceIterator << " (dist: " << targetsIt->EuclideanDistanceTo(transform->TransformPoint(*minDistanceIterator)) << ", minDist: " << minDistance << ")";
sortedSources.push_back(*minDistanceIterator); // this point is assigned
sources.erase(minDistanceIterator); // erase it from sources to avoid duplicate assigns
}
//for (LandmarkPointContainer::const_iterator sortedSourcesIt = sortedSources.begin(); targetsIt != sortedSources.end(); ++targetsIt)
sources = sortedSources;
return true;
}
int mitkPyramidImageRegistrationMethodTest( int argc, char* argv[] )
{
if( argc < 4 )
{
MITK_ERROR << "Not enough input \n Usage: <TEST_NAME> fixed moving type [output_image [output_transform]]"
<< "\n \t fixed : the path to the fixed image \n"
<< " \t moving : path to the image to be registered"
<< " \t type : Affine or Rigid defining the type of the transformation"
<< " \t output_image : output file optional, (full) path, and optionally output_transform : also (full)path to file";
return EXIT_FAILURE;
}
MITK_TEST_BEGIN("PyramidImageRegistrationMethodTest");
mitk::Image::Pointer fixedImage = dynamic_cast<mitk::Image*>(mitk::IOUtil::Load( argv[1] )[0].GetPointer());
mitk::Image::Pointer movingImage = dynamic_cast<mitk::Image*>(mitk::IOUtil::Load( argv[2] )[0].GetPointer());
std::string type_flag( argv[3] );
mitk::PyramidImageRegistrationMethod::Pointer registrationMethod = mitk::PyramidImageRegistrationMethod::New();
registrationMethod->SetFixedImage( fixedImage );
registrationMethod->SetMovingImage( movingImage );
if( type_flag == "Rigid" )
{
registrationMethod->SetTransformToRigid();
}
else if( type_flag == "Affine" )
{
registrationMethod->SetTransformToAffine();
}
else
{
MITK_WARN << " No type specified, using 'Affine' .";
}
registrationMethod->Update();
bool imageOutput = false;
bool transformOutput = false;
std::string image_out_filename, transform_out_filename;
std::string first_output( argv[4] );
// check for txt, otherwise suppose it is an image
if( first_output.find(".txt") != std::string::npos )
{
transformOutput = true;
transform_out_filename = first_output;
}
else
{
imageOutput = true;
image_out_filename = first_output;
}
if( argc > 4 )
{
std::string second_output( argv[5] );
if( second_output.find(".txt") != std::string::npos )
{
transformOutput = true;
transform_out_filename = second_output;
}
}
MITK_INFO << " Selected output: " << transform_out_filename << " " << image_out_filename;
try{
unsigned int paramCount = registrationMethod->GetNumberOfParameters();
double* params = new double[ paramCount ];
registrationMethod->GetParameters( ¶ms[0] );
std::cout << "Parameters: ";
for( unsigned int i=0; i< paramCount; i++)
{
std::cout << params[ i ] << " ";
}
std::cout << std::endl;
if( imageOutput )
{
mitk::IOUtil::Save( registrationMethod->GetResampledMovingImage(), image_out_filename.c_str() );
}
if( transformOutput )
{
itk::TransformFileWriter::Pointer writer = itk::TransformFileWriter::New();
// Get transform parameter for resampling / saving
// Affine
if( paramCount == 12 )
{
typedef itk::AffineTransform< double > TransformType;
TransformType::Pointer transform = TransformType::New();
TransformType::ParametersType affine_params( paramCount );
registrationMethod->GetParameters( &affine_params[0] );
transform->SetParameters( affine_params );
writer->SetInput( transform );
}
// Rigid
else
{
typedef itk::Euler3DTransform< double > RigidTransformType;
RigidTransformType::Pointer rtransform = RigidTransformType::New();
RigidTransformType::ParametersType rigid_params( paramCount );
registrationMethod->GetParameters( &rigid_params[0] );
rtransform->SetParameters( rigid_params );
writer->SetInput( rtransform );
}
writer->SetFileName( transform_out_filename );
writer->Update();
}
}
catch( const std::exception &e)
{
MITK_ERROR << "Caught exception: " << e.what();
}
MITK_TEST_END();
}
int main (int argc, char **argv)
{
int verbose=0, clobber=0,skip_grid=0;
double max=5.0;
double extent=300;
static struct option long_options[] = {
{"verbose", no_argument, &verbose, 1},
{"quiet", no_argument, &verbose, 0},
{"clobber", no_argument, &clobber, 1},
{"spacing", required_argument, 0, 's'},
{"max", required_argument, 0, 'm'},
{"version", no_argument, 0, 'v'},
{"extent", required_argument, 0, 'e'},
{0, 0, 0, 0}
};
double spacing=4.0;
for (;;) {
/* getopt_long stores the option index here. */
int option_index = 0;
int c = getopt_long (argc, argv, "s:m:v", long_options, &option_index);
/* Detect the end of the options. */
if (c == -1) break;
switch (c)
{
case 0:
break;
case 's':
spacing=atof(optarg);
break;
case 'v':
cout << "Version: 1.0" << endl;
return 0;
case 'm':
max=atof(optarg); break;
case 'e':
extent=atof(optarg); break;
case '?':
/* getopt_long already printed an error message. */
default:
show_usage ();
return 1;
}
}
if ((argc - optind) < 2) {
show_usage ();
return 1;
}
std::string input=argv[optind];
std::string output=argv[optind+1];
try
{
gsl_rng_env_setup();
typedef minc::SphericalHarmonicsTransform TransformType;
TransformType::ParametersType finalParameters;
load_parameters(input.c_str(),finalParameters);
TransformType::Pointer finalTransform = TransformType::New();
cout<<"Loaded parameters:"<<finalParameters<<endl;
finalTransform->ImportParameters( finalParameters , true);
cout<<"Imported!"<<endl;
minc::def3d::Pointer grid(minc::def3d::New());
allocate_image3d<minc::def3d>(grid,
fixed_vec<3, unsigned int>(extent/spacing),
fixed_vec<3, double>(spacing),
fixed_vec<3, double>(-extent/2));
if(verbose)
{
cout<<"Generating a grid file, ";
cout<<"extent: "<<extent<<" spacing: "<<spacing<<" ..."<<std::flush;
}
def3d_iterator it(grid,grid->GetLargestPossibleRegion());
for(it.GoToBegin();!it.IsAtEnd();++it)
{
tag_point p,p2;
grid->TransformIndexToPhysicalPoint(it.GetIndex(),p);
p2=finalTransform->TransformPointUnCached(p);
def_vector moved;
moved[0]=p2[0]-p[0];
moved[1]=p2[1]-p[1];
moved[2]=p2[2]-p[2];
if(fabs(moved[0])>max || fabs(moved[1])>max ||fabs(moved[2])>max)
moved[0]=moved[1]=moved[2]=0.0;
it.Value()=moved;
}
if(verbose)
cout<<"Done!"<<endl;
save_minc<def3d>(output.c_str(), grid);
} catch (const minc::generic_error & err) {
cerr << "Got an error at:" << err.file () << ":" << err.line () << endl;
return 1;
}
catch( itk::ExceptionObject & err )
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return 2;
}
return 0;
}
RealImage::Pointer bsplineRegistration(RealImage::Pointer srcImg, RealImage::Pointer dstImg) {
const unsigned int SpaceDimension = ImageDimension;
const unsigned int SplineOrder = 3;
typedef double CoordinateRepType;
typedef itk::BSplineTransform<CoordinateRepType, SpaceDimension, SplineOrder> TransformType;
typedef itk::LBFGSOptimizer OptimizerType;
typedef itk::MeanSquaresImageToImageMetric<ImageType, ImageType> MetricType;
typedef itk::LinearInterpolateImageFunction<ImageType, double> InterpolatorType;
typedef itk::ImageRegistrationMethod<ImageType, ImageType> RegistrationType;
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
// The old registration framework has problems with multi-threading
// For now, we set the number of threads to 1
// registration->SetNumberOfThreads(1);
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
registration->SetInterpolator( interpolator );
TransformType::Pointer transform = TransformType::New();
registration->SetTransform( transform );
// Setup the registration
registration->SetFixedImage( dstImg );
registration->SetMovingImage( srcImg);
ImageType::RegionType fixedRegion = srcImg->GetBufferedRegion();
registration->SetFixedImageRegion( fixedRegion );
// Here we define the parameters of the BSplineDeformableTransform grid. We
// arbitrarily decide to use a grid with $5 \times 5$ nodes within the image.
// The reader should note that the BSpline computation requires a
// finite support region ( 1 grid node at the lower borders and 2
// grid nodes at upper borders). Therefore in this example, we set
// the grid size to be $8 \times 8$ and place the grid origin such that
// grid node (1,1) coincides with the first pixel in the fixed image.
TransformType::PhysicalDimensionsType fixedPhysicalDimensions;
TransformType::MeshSizeType meshSize;
for (unsigned int i=0; i < ImageDimension; i++) {
fixedPhysicalDimensions[i] = dstImg->GetSpacing()[i] *
static_cast<double>(dstImg->GetLargestPossibleRegion().GetSize()[i] - 1 );
meshSize[i] = dstImg->GetLargestPossibleRegion().GetSize()[i] / 8 - SplineOrder;
}
// unsigned int numberOfGridNodesInOneDimension = 15;
// meshSize.Fill( numberOfGridNodesInOneDimension - SplineOrder );
transform->SetTransformDomainOrigin( dstImg->GetOrigin() );
transform->SetTransformDomainPhysicalDimensions( fixedPhysicalDimensions );
transform->SetTransformDomainMeshSize( meshSize );
transform->SetTransformDomainDirection( dstImg->GetDirection() );
typedef TransformType::ParametersType ParametersType;
const unsigned int numberOfParameters = transform->GetNumberOfParameters();
ParametersType parameters( numberOfParameters );
parameters.Fill( 0.0 );
transform->SetParameters( parameters );
// We now pass the parameters of the current transform as the initial
// parameters to be used when the registration process starts.
registration->SetInitialTransformParameters( transform->GetParameters() );
std::cout << "Intial Parameters = " << std::endl;
std::cout << transform->GetParameters() << std::endl;
// Next we set the parameters of the LBFGS Optimizer.
optimizer->SetGradientConvergenceTolerance(0.1);
optimizer->SetLineSearchAccuracy(0.09);
optimizer->SetDefaultStepLength(.1);
optimizer->TraceOn();
optimizer->SetMaximumNumberOfFunctionEvaluations(1000);
std::cout << std::endl << "Starting Registration" << std::endl;
try {
registration->Update();
std::cout << "Optimizer stop condition = "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
} catch (itk::ExceptionObject & err) {
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return RealImage::Pointer();
}
OptimizerType::ParametersType finalParameters =
registration->GetLastTransformParameters();
std::cout << "Last Transform Parameters" << std::endl;
std::cout << finalParameters << std::endl;
transform->SetParameters( finalParameters );
typedef itk::ResampleImageFilter<ImageType, ImageType> ResampleFilterType;
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform( transform );
resample->SetInput( srcImg );
resample->SetSize( dstImg->GetLargestPossibleRegion().GetSize() );
resample->SetOutputOrigin( dstImg->GetOrigin() );
resample->SetOutputSpacing( dstImg->GetSpacing() );
resample->SetOutputDirection( dstImg->GetDirection() );
resample->SetDefaultPixelValue( 100 );
resample->Update();
return resample->GetOutput();
}
void SetTransformParameters(TransformType::Pointer inputTransform) {
transform->SetParameters( inputTransform->GetParameters() );
transform->SetFixedParameters( inputTransform->GetFixedParameters() );
buildSlices();
buildMaskSlices();
}
