void QmitkTensorReconstructionView::TensorsToQbi()
{
for (int i=0; i<m_TensorImages->size(); i++)
{
mitk::DataNode::Pointer tensorImageNode = m_TensorImages->at(i);
MITK_INFO << "starting Q-Ball estimation";
typedef float TTensorPixelType;
typedef itk::DiffusionTensor3D< TTensorPixelType > TensorPixelType;
typedef itk::Image< TensorPixelType, 3 > TensorImageType;
TensorImageType::Pointer itkvol = TensorImageType::New();
mitk::CastToItkImage<TensorImageType>(dynamic_cast<mitk::TensorImage*>(tensorImageNode->GetData()), itkvol);
typedef itk::TensorImageToQBallImageFilter< TTensorPixelType, TTensorPixelType > FilterType;
FilterType::Pointer filter = FilterType::New();
filter->SetInput( itkvol );
filter->Update();
typedef itk::Vector<TTensorPixelType,QBALL_ODFSIZE> OutputPixelType;
typedef itk::Image<OutputPixelType,3> OutputImageType;
mitk::QBallImage::Pointer image = mitk::QBallImage::New();
OutputImageType::Pointer outimg = filter->GetOutput();
image->InitializeByItk( outimg.GetPointer() );
image->SetVolume( outimg->GetBufferPointer() );
mitk::DataNode::Pointer node = mitk::DataNode::New();
node->SetData( image );
QString newname;
newname = newname.append(tensorImageNode->GetName().c_str());
newname = newname.append("_qbi");
node->SetName(newname.toAscii());
GetDefaultDataStorage()->Add(node);
}
}
mitk::TensorImageSource::TensorImageSource()
{
// Create the output. We use static_cast<> here because we know the default
// output must be of type TOutputImage
OutputImageType::Pointer output
= static_cast<OutputImageType*>(this->MakeOutput(0).GetPointer());
Superclass::SetNumberOfRequiredOutputs(1);
Superclass::SetNthOutput(0, output.GetPointer());
}
// ------------------------------------------------------------------------
void OpenCVValve::ConvertImage(const ImageType::Pointer &input, MatPtr &mat)
{
// cast the image to uchar
typedef itk::Image<unsigned char, 2> OutputImageType;
typedef itk::RescaleIntensityImageFilter<ImageType, OutputImageType> CasterType;
CasterType::Pointer caster = CasterType::New();
caster->SetOutputMaximum(255);
caster->SetOutputMinimum(0);
caster->SetInput(input);
caster->Update();
OutputImageType::Pointer output = caster->GetOutput();
typedef itk::ImageFileWriter<OutputImageType> WriterType;
WriterType::Pointer writer = WriterType::New();
writer->SetImageIO(itk::PNGImageIO::New());
writer->SetInput(output);
writer->SetFileName("test.png");
writer->Update();
ImageType::SizeType size = input->GetLargestPossibleRegion().GetSize();
unsigned int rows = size[1];
unsigned int cols = size[0];
mat = new MatType(rows,cols, CV_8UC1);
itk::ImageRegionConstIterator<OutputImageType> it(output, output->GetLargestPossibleRegion());
it.GoToBegin();
while(!it.IsAtEnd())
{
OutputImageType::IndexType index = it.GetIndex();
unsigned char val = it.Get();
mat->at<unsigned char>(cv::Point(index[0], index[1])) = val;
++it;
}
}
void itk::ConnectomicsNetworkToConnectivityMatrixImageFilter::GenerateData()
{
this->AllocateOutputs();
OutputImageType::Pointer output = this->GetOutput();
if(m_InputNetwork.IsNull())
{
MITK_ERROR << "Failed to generate data, no valid network was set.";
return;
}
typedef mitk::ConnectomicsNetwork::NetworkType NetworkType;
typedef boost::graph_traits< NetworkType >::vertex_descriptor DescriptorType;
typedef boost::graph_traits< NetworkType >::vertex_iterator IteratorType;
// prepare connectivity matrix for data
std::vector< std::vector< int > > connectivityMatrix;
int numberOfVertices = m_InputNetwork->GetNumberOfVertices();
connectivityMatrix.resize( numberOfVertices );
for( int index(0); index < numberOfVertices; index++ )
{
connectivityMatrix[ index ].resize( numberOfVertices );
}
// Create LabelToIndex translation
std::map< std::string, DescriptorType > labelToIndexMap;
NetworkType boostGraph = *(m_InputNetwork->GetBoostGraph());
// translate index to label
IteratorType iterator, end;
boost::tie(iterator, end) = boost::vertices( boostGraph );
for ( ; iterator != end; ++iterator)
{
labelToIndexMap.insert(
std::pair< std::string, DescriptorType >(
boostGraph[ *iterator ].label, *iterator )
);
}
std::vector< std::string > indexToLabel;
// translate index to label
indexToLabel.resize( numberOfVertices );
boost::tie(iterator, end) = boost::vertices( boostGraph );
for ( ; iterator != end; ++iterator)
{
indexToLabel[ *iterator ] = boostGraph[ *iterator ].label;
}
//translate label to position
std::vector< std::string > positionToLabelVector;
positionToLabelVector = indexToLabel;
std::sort ( positionToLabelVector.begin(), positionToLabelVector.end() );
for( int outerLoop( 0 ); outerLoop < numberOfVertices; outerLoop++ )
{
DescriptorType fromVertexDescriptor = labelToIndexMap.find( positionToLabelVector[ outerLoop ] )->second;
for( int innerLoop( outerLoop + 1 ); innerLoop < numberOfVertices; innerLoop++ )
{
DescriptorType toVertexDescriptor = labelToIndexMap.find( positionToLabelVector[ innerLoop ] )->second;
int weight( 0 );
if( boost::edge(toVertexDescriptor, fromVertexDescriptor, boostGraph ).second )
{
weight = m_InputNetwork->GetEdge( fromVertexDescriptor , toVertexDescriptor ).weight;;
}
connectivityMatrix[outerLoop][innerLoop] = weight;
connectivityMatrix[innerLoop][outerLoop] = weight;
}
}
OutputImageType::SpacingType spacing;
spacing[0] = 1.0;
spacing[1] = 1.0;
OutputImageType::PointType origin;
origin[0] = 0.0;
origin[1] = 0.0;
OutputImageType::IndexType index;
index[0] = 0.0;
index[1] = 0.0;
OutputImageType::SizeType size;
size[0] = numberOfVertices;
size[1] = numberOfVertices;
OutputImageType::RegionType region;
region.SetIndex( index );
region.SetSize( size );
output->SetSpacing( spacing ); // Set the image spacing
output->SetOrigin( origin ); // Set the image origin
output->SetRegions( region );
output->Allocate();
output->FillBuffer(0);
itk::ImageRegionIterator< OutputImageType > it_connect(output, output->GetLargestPossibleRegion());
int counter( 0 );
double rescaleFactor( 1.0 );
double rescaleMax( 255.0 );
if( m_RescaleConnectivity )
{
rescaleFactor = rescaleMax / m_InputNetwork->GetMaximumWeight();
}
// Colour pixels according to connectivity
if( m_BinaryConnectivity )
{
// binary connectivity
while( !it_connect.IsAtEnd() )
{
if( connectivityMatrix[ ( counter - counter % numberOfVertices ) / numberOfVertices][ counter % numberOfVertices ] )
{
it_connect.Set( 1 );
}
else
{
it_connect.Set( 0 );
}
++it_connect;
counter++;
}
}
else
{
// if desired rescale to the 0-255 range
while( !it_connect.IsAtEnd() )
{
it_connect.Set( ( unsigned short ) rescaleFactor *
connectivityMatrix[ ( counter - counter % numberOfVertices ) / numberOfVertices][ counter % numberOfVertices ]
);
++it_connect;
counter++;
}
}
}
int runGrAnisDiff(unsigned char* imgIn, int r, int c, int z, int iter, int timeStep, int comductance)
{
//pixel types
typedef unsigned char InputPixelType;
typedef float OutputPixelType;
typedef itk::Image< InputPixelType, 3 > InputImageType;
typedef itk::Image< OutputPixelType, 3 > OutputImageType;
//create an ITK image from the input image
OutputImageType::Pointer im;
im = OutputImageType::New();
OutputImageType::PointType origin;
origin[0] = 0.;
origin[1] = 0.;
origin[2] = 0.;
im->SetOrigin( origin );
OutputImageType::IndexType start;
start[0] = 0; // first index on X
start[1] = 0; // first index on Y
start[2] = 0; // first index on Z
OutputImageType::SizeType size;
size[0] = c; // size along X
size[1] = r; // size along Y
size[2] = z; // size along Z
OutputImageType::RegionType region;
region.SetSize( size );
region.SetIndex( start );
// double spacing[3];
//spacing[0] = 1; //spacing along x
//spacing[1] = 1; //spacing along y
//spacing[2] = sampl_ratio; //spacing along z
im->SetRegions( region );
//im->SetSpacing(spacing);
im->Allocate();
im->FillBuffer(0);
im->Update();
//copy the input image into the ITK image
typedef itk::ImageRegionIteratorWithIndex< OutputImageType > IteratorType;
IteratorType iterator1(im,im->GetRequestedRegion());
for(int i=0; i<r*c*z; i++)
{
iterator1.Set((float)imgIn[i]);
++iterator1;
}
//apply gradient anisotropic diffusion
typedef itk::GradientAnisotropicDiffusionImageFilter< OutputImageType, OutputImageType > FilterType;
FilterType::Pointer filter = FilterType::New();
filter->SetInput( im );
filter->SetNumberOfIterations( iter );
filter->SetTimeStep( timeStep );
filter->SetConductanceParameter( comductance );
try
{
filter->Update();
}
catch( itk::ExceptionObject & err )
{
std::cerr << err << std::endl;
return 0;
}
typedef itk::RescaleIntensityImageFilter< OutputImageType, InputImageType > RescaleFilterType;
RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum( 0 );
rescaler->SetOutputMaximum( 255 );
rescaler->SetInput( filter->GetOutput() );
rescaler->Update();
//overwrite the input image for now in order to save space
typedef itk::ImageRegionIteratorWithIndex< InputImageType > IteratorType2;
IteratorType2 iterator2(rescaler->GetOutput(),rescaler->GetOutput()->GetRequestedRegion());
for(int i=0; i<r*c*z; i++)
{
imgIn[i] = iterator2.Get();
++iterator2;
}
return 1;
}
int main(int argc, char** argv) {
if (argc < 5) {
std::cerr << "From the 3 images representing fiber orientation estimate angles and calculate angle homogeneity in the neighborhoods." << std::endl;
std::cerr << "Usage: CalculateEntropyFiberAngles coordx_image coordy_image coordz_image out_image neigborhood_radius" << std::endl;
return EXIT_FAILURE;
}
//parsing parameters
int c = 1;
const char* imagex_filename = argv[c++];
const char* imagey_filename = argv[c++];
const char* imagez_filename = argv[c++];
const char* output_filename = argv[c++];
int neighborhood_radius = atoi(argv[c++]);
const unsigned int Dimension = 3;
std::cout << "Input x: " << imagex_filename << std::endl;
std::cout << "Input y: " << imagey_filename << std::endl;
std::cout << "Input z: " << imagez_filename << std::endl;
std::cout << "Output: " << output_filename << std::endl;
std::cout << "Neighborhood radius: " << neighborhood_radius << std::endl;
//main program
typedef float InputPixelType;
typedef float OutputPixelType;
typedef itk::Image< InputPixelType, Dimension > InputImageType;
typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
typedef itk::ImageFileReader< InputImageType > ReaderType;
typedef itk::ImageFileWriter< OutputImageType > WriterType;
ReaderType::Pointer readerx = ReaderType::New();
ReaderType::Pointer readery = ReaderType::New();
ReaderType::Pointer readerz = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
readerx->SetFileName(imagex_filename);
readery->SetFileName(imagex_filename);
readerz->SetFileName(imagex_filename);
writer->SetFileName(output_filename);
std::cout<<"Reading X"<<std::endl;
readerx->Update();
std::cout<<"Reading Y"<<std::endl;
readery->Update();
std::cout<<"Reading Z"<<std::endl;
readerz->Update();
const InputImageType * inputx = readerx->GetOutput();
const InputImageType * inputy = readery->GetOutput();
const InputImageType * inputz = readerz->GetOutput();
//create the output image
OutputImageType::Pointer image = OutputImageType::New();
image->SetRegions(inputx->GetLargestPossibleRegion());
image->SetSpacing(inputx->GetSpacing());
image->SetOrigin(inputx->GetOrigin());
image->Allocate();
image->FillBuffer(0);
InputImageType::SizeType radius;
radius[0] = neighborhood_radius;
radius[1] = neighborhood_radius;
radius[2] = neighborhood_radius;
itk::ConstNeighborhoodIterator<InputImageType>
iteratorx(radius, inputx, image->GetRequestedRegion());
itk::ConstNeighborhoodIterator<InputImageType>
iteratory(radius, inputy, image->GetRequestedRegion());
itk::ConstNeighborhoodIterator<InputImageType>
iteratorz(radius, inputz, image->GetRequestedRegion());
itk::ImageRegionIterator<OutputImageType> it( image, image->GetRequestedRegion() );
iteratorx.GoToBegin();
iteratory.GoToBegin();
iteratorz.GoToBegin();
it.GoToBegin();
const unsigned int nneighbors = (neighborhood_radius*2+1)*(neighborhood_radius*2+1)*(neighborhood_radius*2+1);
Eigen::MatrixXf neighbors( nneighbors-1, 3 ); //to store the vectors
Eigen::Vector3f central_vector;
int c_id = (int) (nneighbors / 2); // get offset of center pixel
//check image size for the progress indicator
OutputImageType::RegionType region = image->GetLargestPossibleRegion();
OutputImageType::SizeType size = region.GetSize();
const unsigned long int npixels = size[0]*size[1]*size[2];
unsigned long int k = 0;
while ( ! iteratorx.IsAtEnd() )
{
if(k%10000 == 0)
{
std::cout<<"Processed "<<k<<"/"<<npixels<<"\r"<<std::flush;
}
k++;
unsigned int j = 0;
for (unsigned int i = 0; i < nneighbors; ++i)
{
if(i!=c_id) //exclude the center
{
neighbors(j, 0) = iteratorx.GetPixel(i);
neighbors(j, 1) = iteratory.GetPixel(i);
neighbors(j, 2) = iteratorz.GetPixel(i);
j++;
}
else
{
central_vector(0) = iteratorx.GetPixel(i);
central_vector(1) = iteratory.GetPixel(i);
central_vector(2) = iteratorz.GetPixel(i);
}
}
// calculate the angles
Eigen::VectorXf dp = neighbors*central_vector;
const float variance = ((dp.array()-dp.mean()).pow(2)).mean();
it.Set(variance);
++iteratorx;
++iteratory;
++iteratorz;
++it;
}
std::cout<<std::endl;
// The output of the resampling filter is connected to a writer and the
// execution of the pipeline is triggered by a writer update.
try {
writer->SetInput(image);
writer->Update();
} catch (itk::ExceptionObject & excep) {
std::cerr << "Exception caught !" << std::endl;
std::cerr << excep << std::endl;
}
return EXIT_SUCCESS;
}
void segmentation::on_abnormalRegionSegButton_clicked()
{
fstream fin11, fin12;
char* s11Txt = ("../data/ped/txt/15715_11.txt");//s11.txt
char* s12Txt = ("../data/ped/txt/15715_12.txt");
int (*s11)[512] = new int[64][512];
int (*s12)[512] = new int[64][512];
int (*p)[512] = new int[64][512];
//read TXT
fin11.open(s11Txt, ios::in);
fin12.open(s12Txt, ios::in);
//get the p matrix
for(int z = 0; z < 64; z++)
{
for(int x = 0; x < 512; x++)
{
fin11>>s11[z][x];
fin12>>s12[z][x];
if(s12[z][x] - s11[z][x] > 5) //5 threshold
p[z][x] = 1;
else
p[z][x] = 0;
}
}
fin11.close();
fin12.close();
//read filtered MHD data
typedef itk::Image<unsigned short, 3>InputImageType;
typedef itk::Image<unsigned short, 3>OutputImageType;
typedef itk::Image<unsigned char, 3>InitOutputImageType;
InputImageType::Pointer inputImage = InputImageType::New();
OutputImageType::Pointer outputImage = OutputImageType::New();
InitOutputImageType::Pointer initOutputImage = InitOutputImageType::New();
typedef itk::ImageFileReader<InputImageType>ReaderType;
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(inputFileName);//读入原图像
reader->Update();
inputImage = reader->GetOutput();
InputImageType::IndexType voxelIndex;
InitOutputImageType::IndexType initvoxelIndex;
OutputImageType::IndexType newvoxelIndex;
InputImageType::SizeType imgSize = inputImage->GetLargestPossibleRegion().GetSize();
OutputImageType::IndexType index;
index[0] = 0;
index[1] = 0;
index[2] = 0;
OutputImageType::SizeType size;
size[0] = imgSize[0];
size[1] = imgSize[1];
size[2] = imgSize[2];
//create a region for initial result
InitOutputImageType::RegionType initRegion;
initRegion.SetIndex(index);
initRegion.SetSize(size);
initOutputImage->SetRegions( initRegion);
initOutputImage->Allocate();
//create a region for enhance result
OutputImageType::RegionType region;
region.SetIndex(index);
region.SetSize(size);
outputImage->SetRegions(region);
outputImage->Allocate();
//Initial result for PED segmentation (a binary image)
for(int z = 0; z < imgSize[2]; z++)
for(int x = 0; x < imgSize[0]; x++)
{
initvoxelIndex[0] = x;
initvoxelIndex[2] = z;
for(int y = 0; y < imgSize[1]; y++)
{
//set all background a black region
initvoxelIndex[1] = y;
initOutputImage->SetPixel(initvoxelIndex, 0);
}
//set the same intensity for all PED region (empirical value)
if(p[z][x] == 1)
{
for(int y = s11[z][x]; y <= s12[z][x]; y++)
{
initvoxelIndex[1] = y;
initOutputImage->SetPixel(initvoxelIndex, 255);//亮区域
}
}
}
//输出中间分割结果
//文件前缀名
filePrefix = inputFileName;//char* to string
filePrefix = filePrefix.substr(0, filePrefix.length() - 4);
string strInitFileName;
strInitFileName = filePrefix + "_initBinaryImg.mhd";
strcpy(initFileName, strInitFileName.c_str());//string to char*
typedef itk::ImageFileWriter<InitOutputImageType>InitWriterType;
InitWriterType::Pointer initWriter = InitWriterType::New();
initWriter->SetFileName(initFileName);//生成二值图
initWriter->SetInput(initOutputImage);
initWriter->Update();
//Enhance PED region and overlay it on the original image
for(int z = 0; z < imgSize[2]; z++)
for(int x = 0; x < imgSize[0]; x++)
{
voxelIndex[0] = x;
voxelIndex[2] = z;
newvoxelIndex[0] = x;
newvoxelIndex[2] = z;
for(int y = 0; y < imgSize[1]; y++)
{
voxelIndex[1] = y;
newvoxelIndex[1] = y;
outputImage->SetPixel(newvoxelIndex, inputImage->GetPixel(voxelIndex));
}
//set the same intensity for all PED region (empirical value)
if(p[z][x] == 1)
{
for(int y = s11[z][x]; y <= s12[z][x]; y++)
{
newvoxelIndex[1] = y;
outputImage->SetPixel(newvoxelIndex, 65535);//亮区域
}
}
}
//释放内存不能缺省
delete[]s11;
delete[]s12;
delete[]p;
typedef itk::ImageFileWriter<OutputImageType>WriterType;
WriterType::Pointer writer = WriterType::New();
filePrefix = filePrefix + "_initResult.mhd";
strcpy(outputFileName, filePrefix.c_str());//string to char*
initResultFileName = outputFileName;//输出初始分割结果，二值图重叠在原图像上面
writer->SetFileName(outputFileName);
writer->SetInput(outputImage);
writer->Update();
emit returnOutputFileName(outputFileName);//发出信号
}
void IntrinsicFeatureCalculator::GetDistanceToSurfaceMeasures(vtkSmartPointer<vtkTable> table, std::vector< ftk::Object::Point > surfacePoints)
{
typedef itk::Image< IPixelT, 3 > InputImageType;
typedef itk::Image< LPixelT, 3 > OutputImageType;
typedef itk::DanielssonDistanceMapImageFilter< OutputImageType, OutputImageType > DanielssonFilterType;
typedef itk::RescaleIntensityImageFilter< OutputImageType, OutputImageType > RescalerType;
typedef itk::ImageFileReader< InputImageType > ReaderType;
typedef itk::ImageFileWriter< InputImageType > InputWriterType;
typedef itk::ImageFileWriter< OutputImageType > WriterType;
typedef itk::LineIterator< OutputImageType > LineIteratorType;
vtkSmartPointer<vtkDoubleArray> column = vtkSmartPointer<vtkDoubleArray>::New();
column->SetName( "Dist_To_Surface" );
column->SetNumberOfValues( table->GetNumberOfRows() );
table->AddColumn(column);
OutputImageType::Pointer im;
im = OutputImageType::New();
OutputImageType::PointType origin;
origin[0] = 0;
origin[1] = 0;
origin[2] = 0;
im->SetOrigin( origin );
OutputImageType::IndexType start;
start[0] = 0; // first index on X
start[1] = 0; // first index on Y
start[2] = 0; // first index on Z
OutputImageType::Pointer temp_image = labelImage->GetItkPtr< LPixelT >(0,0);
itk::Size<3> im_size = temp_image->GetBufferedRegion().GetSize();
im_size[2] = 1;
InputImageType::RegionType region;
region.SetSize( im_size );
region.SetIndex( start );
im->SetRegions( region );
im->Allocate();
im->FillBuffer(0);
im->Update();
//copy the input image into the ITK image
for(int p = 1; p < (int)surfacePoints.size(); ++p)
{
itk::Index<3> indx,indy;
indx[0] = surfacePoints[p-1].x;
indx[1] = surfacePoints[p-1].y;
indx[2] = 0;
indy[0] = surfacePoints[p].x;
indy[1] = surfacePoints[p].y;
indy[2] = 0;
LineIteratorType it( im, indx, indy );
//it.GoToBegin();
//while(!it.IsAtEnd())
for(it.GoToBegin(); !it.IsAtEnd(); ++it)
{
it.Set(255);
}
}
DanielssonFilterType::Pointer danielssonFilter = DanielssonFilterType::New();
WriterType::Pointer writer = WriterType::New();
danielssonFilter->SetInput( im );
writer->SetFileName( "DistanceMap.tif" );
danielssonFilter->InputIsBinaryOn();
danielssonFilter->Update();
OutputImageType::Pointer distMap = danielssonFilter->GetOutput();
writer->SetInput( distMap );
writer->Update();
for(int row=0; row<(int)table->GetNumberOfRows(); ++row)
{
OutputImageType::IndexType indx;
indx[0] = table->GetValue(row, 1).ToInt();
indx[1] = table->GetValue(row, 2).ToInt();
indx[2] = 0;
int dist = distMap->GetPixel(indx);
table->SetValueByName(row, "Dist_To_Surface", vtkVariant(dist));
}
}