void FiniteDiffOdfMaximaExtractionFilter< PixelType, ShOrder, NrOdfDirections>
::ThreadedGenerateData( const OutputImageRegionType& outputRegionForThread, ThreadIdType threadID )
{
typename CoefficientImageType::Pointer ShCoeffImage = static_cast< CoefficientImageType* >( this->ProcessObject::GetInput(0) );
ImageRegionConstIterator< CoefficientImageType > cit(ShCoeffImage, outputRegionForThread );
OdfType odf;
while( !cit.IsAtEnd() )
{
typename CoefficientImageType::IndexType index = cit.GetIndex();
if (m_MaskImage->GetPixel(index)==0)
{
++cit;
continue;
}
CoefficientPixelType c = cit.Get();
// calculate ODF
double max = 0;
odf.Fill(0.0);
for (int i=0; i<NrOdfDirections; i++)
{
for (int j=0; j<m_NumCoeffs; j++)
odf[i] += c[j]*m_ShBasis(i,j);
if (odf[i]>max)
max = odf[i];
}
if (max<0.0001)
{
++cit;
continue;
}
std::vector< DirectionType > candidates, peaks, temp;
peaks.clear();
max *= m_PeakThreshold; // relative threshold
FindCandidatePeaks(odf, max, candidates); // find all local maxima
candidates = MeanShiftClustering(candidates); // cluster maxima
vnl_matrix< double > shBasis, sphCoords;
Cart2Sph(candidates, sphCoords); // convert candidate peaks to spherical angles
shBasis = CalcShBasis(sphCoords); // evaluate spherical harmonics at each peak
max = 0.0;
for (unsigned int i=0; i<candidates.size(); i++) // scale peaks according to ODF value
{
double val = 0;
for (int j=0; j<m_NumCoeffs; j++)
val += c[j]*shBasis(i,j);
if (val>max)
max = val;
peaks.push_back(candidates[i]*val);
}
std::sort( peaks.begin(), peaks.end(), CompareVectors ); // sort peaks
// kick out directions to close to a larger direction (too far away to cluster but too close to keep)
unsigned int m = peaks.size();
if ( m>m_DirectionImageContainer->Size() )
m = m_DirectionImageContainer->Size();
for (unsigned int i=0; i<m; i++)
{
DirectionType v1 = peaks.at(i);
double val = v1.magnitude();
if (val<max*m_PeakThreshold || val<m_AbsolutePeakThreshold)
break;
bool flag = true;
for (unsigned int j=0; j<peaks.size(); j++)
if (i!=j)
{
DirectionType v2 = peaks.at(j);
double val2 = v2.magnitude();
double angle = fabs(dot_product(v1,v2)/(val*val2));
if (angle>m_AngularThreshold && val<val2)
{
flag = false;
break;
}
}
if (flag)
temp.push_back(v1);
}
peaks = temp;
// fill output image
unsigned int num = peaks.size();
if ( num>m_DirectionImageContainer->Size() )
num = m_DirectionImageContainer->Size();
for (unsigned int i=0; i<num; i++)
{
vnl_vector<double> dir = peaks.at(i);
ItkDirectionImage::Pointer img = m_DirectionImageContainer->GetElement(i);
switch (m_NormalizationMethod)
{
case NO_NORM:
break;
case SINGLE_VEC_NORM:
dir.normalize();
break;
case MAX_VEC_NORM:
dir /= max;
break;
}
dir = m_MaskImage->GetDirection()*dir;
itk::Vector< float, 3 > pixel;
pixel.SetElement(0, -dir[0]);
pixel.SetElement(1, dir[1]);
pixel.SetElement(2, dir[2]);
img->SetPixel(index, pixel);
}
m_NumDirectionsImage->SetPixel(index, num);
++cit;
}
MITK_INFO << "Thread " << threadID << " finished extraction";
}
void DwiPhantomGenerationFilter< TOutputScalarType >
::GenerateData()
{
if (m_NoiseVariance < 0)
m_NoiseVariance = 0.001;
if (!m_SimulateBaseline)
{
MITK_INFO << "Baseline image values are set to default. Noise variance value is treated as SNR!";
if (m_NoiseVariance <= 0)
m_NoiseVariance = 0.0001;
if (m_NoiseVariance>99)
m_NoiseVariance = 0;
else
{
m_NoiseVariance = m_DefaultBaseline/(m_NoiseVariance*m_SignalScale);
m_NoiseVariance *= m_NoiseVariance;
}
}
m_RandGen = Statistics::MersenneTwisterRandomVariateGenerator::New();
m_RandGen->SetSeed();
typename OutputImageType::Pointer outImage = OutputImageType::New();
outImage->SetSpacing( m_Spacing );
outImage->SetOrigin( m_Origin );
outImage->SetDirection( m_DirectionMatrix );
outImage->SetLargestPossibleRegion( m_ImageRegion );
outImage->SetBufferedRegion( m_ImageRegion );
outImage->SetRequestedRegion( m_ImageRegion );
outImage->SetVectorLength(m_GradientList.size());
outImage->Allocate();
typename OutputImageType::PixelType pix;
pix.SetSize(m_GradientList.size());
pix.Fill(0.0);
outImage->FillBuffer(pix);
this->SetNthOutput (0, outImage);
double minSpacing = m_Spacing[0];
if (m_Spacing[1]<minSpacing)
minSpacing = m_Spacing[1];
if (m_Spacing[2]<minSpacing)
minSpacing = m_Spacing[2];
m_DirectionImageContainer = ItkDirectionImageContainer::New();
for (int i=0; i<m_SignalRegions.size(); i++)
{
itk::Vector< float, 3 > nullVec; nullVec.Fill(0.0);
ItkDirectionImage::Pointer img = ItkDirectionImage::New();
img->SetSpacing( m_Spacing );
img->SetOrigin( m_Origin );
img->SetDirection( m_DirectionMatrix );
img->SetRegions( m_ImageRegion );
img->Allocate();
img->FillBuffer(nullVec);
m_DirectionImageContainer->InsertElement(m_DirectionImageContainer->Size(), img);
}
m_NumDirectionsImage = ItkUcharImgType::New();
m_NumDirectionsImage->SetSpacing( m_Spacing );
m_NumDirectionsImage->SetOrigin( m_Origin );
m_NumDirectionsImage->SetDirection( m_DirectionMatrix );
m_NumDirectionsImage->SetRegions( m_ImageRegion );
m_NumDirectionsImage->Allocate();
m_NumDirectionsImage->FillBuffer(0);
m_SNRImage = ItkFloatImgType::New();
m_SNRImage->SetSpacing( m_Spacing );
m_SNRImage->SetOrigin( m_Origin );
m_SNRImage->SetDirection( m_DirectionMatrix );
m_SNRImage->SetRegions( m_ImageRegion );
m_SNRImage->Allocate();
m_SNRImage->FillBuffer(0);
vtkSmartPointer<vtkCellArray> m_VtkCellArray = vtkSmartPointer<vtkCellArray>::New();
vtkSmartPointer<vtkPoints> m_VtkPoints = vtkSmartPointer<vtkPoints>::New();
m_BaselineImages = 0;
for( unsigned int i=0; i<m_GradientList.size(); i++)
if (m_GradientList[i].GetNorm()<=0.0001)
m_BaselineImages++;
typedef ImageRegionIterator<OutputImageType> IteratorOutputType;
IteratorOutputType it (outImage, m_ImageRegion);
// isotropic tensor
itk::DiffusionTensor3D<float> isoTensor;
isoTensor.Fill(0);
float e1 = m_GreyMatterAdc;
float e2 = m_GreyMatterAdc;
float e3 = m_GreyMatterAdc;
isoTensor.SetElement(0,e1);
isoTensor.SetElement(3,e2);
isoTensor.SetElement(5,e3);
m_MaxBaseline = GetTensorL2Norm(isoTensor);
GenerateTensors();
// simulate measurement
m_MeanBaseline = 0;
double noiseStdev = sqrt(m_NoiseVariance);
while(!it.IsAtEnd())
{
pix = it.Get();
typename OutputImageType::IndexType index = it.GetIndex();
int numDirs = 0;
for (int i=0; i<m_SignalRegions.size(); i++)
{
ItkUcharImgType::Pointer region = m_SignalRegions.at(i);
if (region->GetPixel(index)!=0)
{
numDirs++;
pix += SimulateMeasurement(m_TensorList[i], m_TensorWeight[i]);
// set direction image pixel
ItkDirectionImage::Pointer img = m_DirectionImageContainer->GetElement(i);
itk::Vector< float, 3 > pixel = img->GetPixel(index);
vnl_vector_fixed<double, 3> dir = m_TensorDirection.at(i);
dir.normalize();
dir *= m_TensorWeight.at(i);
pixel.SetElement(0, dir[0]);
pixel.SetElement(1, dir[1]);
pixel.SetElement(2, dir[2]);
img->SetPixel(index, pixel);
vtkSmartPointer<vtkPolyLine> container = vtkSmartPointer<vtkPolyLine>::New();
itk::ContinuousIndex<double, 3> center;
center[0] = index[0];
center[1] = index[1];
center[2] = index[2];
itk::Point<double> worldCenter;
outImage->TransformContinuousIndexToPhysicalPoint( center, worldCenter );
itk::Point<double> worldStart;
worldStart[0] = worldCenter[0]-dir[0]/2 * minSpacing;
worldStart[1] = worldCenter[1]-dir[1]/2 * minSpacing;
worldStart[2] = worldCenter[2]-dir[2]/2 * minSpacing;
vtkIdType id = m_VtkPoints->InsertNextPoint(worldStart.GetDataPointer());
container->GetPointIds()->InsertNextId(id);
itk::Point<double> worldEnd;
worldEnd[0] = worldCenter[0]+dir[0]/2 * minSpacing;
worldEnd[1] = worldCenter[1]+dir[1]/2 * minSpacing;
worldEnd[2] = worldCenter[2]+dir[2]/2 * minSpacing;
id = m_VtkPoints->InsertNextPoint(worldEnd.GetDataPointer());
container->GetPointIds()->InsertNextId(id);
m_VtkCellArray->InsertNextCell(container);
}
}
if (numDirs>1)
{
for (int i=0; i<m_GradientList.size(); i++)
pix[i] /= numDirs;
}
else if (numDirs==0)
{
if (m_SimulateBaseline)
pix = SimulateMeasurement(isoTensor, 1.0);
else
pix.Fill(0.0);
}
m_MeanBaseline += pix[0];
it.Set(pix);
m_NumDirectionsImage->SetPixel(index, numDirs);
if (m_NoiseVariance>0)
m_SNRImage->SetPixel(index, pix[0]/(noiseStdev*m_SignalScale));
++it;
}
m_MeanBaseline /= m_ImageRegion.GetNumberOfPixels();
if (m_NoiseVariance>0)
MITK_INFO << "Mean SNR: " << m_MeanBaseline/(noiseStdev*m_SignalScale);
else
MITK_INFO << "No noise added";
// add rician noise
it.GoToBegin();
while(!it.IsAtEnd())
{
pix = it.Get();
AddNoise(pix);
it.Set(pix);
++it;
}
// generate fiber bundle
vtkSmartPointer<vtkPolyData> directionsPolyData = vtkSmartPointer<vtkPolyData>::New();
directionsPolyData->SetPoints(m_VtkPoints);
directionsPolyData->SetLines(m_VtkCellArray);
m_OutputFiberBundle = mitk::FiberBundleX::New(directionsPolyData);
}