int main(int argc, char* argv[]) {
CSimpleOpt::SOption specs[] = {
{ 0, "-o", SO_REQ_SEP },
SO_END_OF_OPTIONS
};
Options argParser;
StringVector args = argParser.ParseOptions(argc, argv, specs);
if (args.size() < 1) {
cout << argv[0] << " [input-vector] [output-rgb]" << endl;
return 1;
}
ImageIO<DeformFieldImageType> io;
DeformFieldImageType::Pointer img = io.ReadImage(args[0]);
// itk::UnaryFunctorImageFilter<DeformFieldImageType,RGBImageType> CastFilter;
typedef itk::UnaryFunctorImageFilter<DeformFieldImageType, RGBImageType, Vector2RGB> CastFilter;
CastFilter::Pointer caster = CastFilter::New();
caster->SetInput(img);
caster->Update();
RGBImageType::Pointer rgbImg = caster->GetOutput();
rgbImg->Print(cout);
io.WriteImageS<RGBImageType>(args[1], rgbImg);
return 0;
}
int main(int argc, char**argv)
{
typedef itk::GradientAnisotropicDiffusionImageFilter<FloatImageType, FloatImageType> FilterType;
if(!file_exists(argv[3]))
{
InputImageType::Pointer im = readImage<InputImageType>(argv[1]);
typedef itk::CastImageFilter<InputImageType,FloatImageType> CastFilter;
typedef itk::CastImageFilter<FloatImageType,InputImageType> ReverseCastFilter;
FilterType::Pointer filter = FilterType::New();
CastFilter::Pointer cfilter = CastFilter::New();
cfilter->SetInput(im);
filter->SetInput(cfilter->GetOutput());
int num_i;
float time_step;
float conductance;
sscanf(argv[2],"%d,%f,%f",&num_i,&time_step,&conductance);
printf("time_step %f num_i %d conductance %f\n",time_step,num_i,conductance);
filter->SetTimeStep(time_step);
filter->SetNumberOfIterations(num_i);
filter->SetConductanceParameter(conductance);
filter->Update();
FloatImageType::Pointer imf = filter->GetOutput();
typedef itk::ImageRegionIterator<FloatImageType> IRI;
IRI imageIterator(imf,imf->GetLargestPossibleRegion());
for(imageIterator.GoToBegin();!imageIterator.IsAtEnd(); ++imageIterator)
{
float value = imageIterator.Get();
value = ((value < 0)?0:((value>255)?255:value));
imageIterator.Set(value);
}
ReverseCastFilter::Pointer rfilter = ReverseCastFilter::New();
rfilter->SetInput(filter->GetOutput());
rfilter->Update();
writeImage<InputImageType>(rfilter->GetOutput(),argv[3]);
}
return 0;
}
//#################### PUBLIC METHODS ####################
void CTIPFBuilder::execute()
{
typedef itk::Image<short,3> GradientMagnitudeImage;
typedef itk::Image<int,3> HounsfieldImage;
typedef itk::Image<float,3> RealImage;
typedef itk::Image<unsigned char,3> WindowedImage;
HounsfieldImage::Pointer hounsfieldImage = (*m_volume)->base_image();
//~~~~~~~
// STEP 1
//~~~~~~~
set_status("Preprocessing image...");
// Construct the windowed image.
WindowedImage::Pointer windowedImage = (*m_volume)->windowed_image(m_segmentationOptions.windowSettings);
if(is_aborted()) return;
// Cast the input image (whether Hounsfield or windowed) to make its pixels real-valued.
RealImage::Pointer realImage;
switch(m_segmentationOptions.inputType)
{
case CTSegmentationOptions::INPUTTYPE_HOUNSFIELD:
{
typedef itk::CastImageFilter<HounsfieldImage,RealImage> CastFilter;
CastFilter::Pointer castFilter = CastFilter::New();
castFilter->SetInput(hounsfieldImage);
castFilter->Update();
realImage = castFilter->GetOutput();
break;
}
case CTSegmentationOptions::INPUTTYPE_WINDOWED:
{
typedef itk::CastImageFilter<WindowedImage,RealImage> CastFilter;
CastFilter::Pointer castFilter = CastFilter::New();
castFilter->SetInput(windowedImage);
castFilter->Update();
realImage = castFilter->GetOutput();
break;
}
default:
{
throw Exception("Unknown CT segmentation input type"); // this should never happen
}
}
if(is_aborted()) return;
// Smooth this real image using anisotropic diffusion filtering.
typedef itk::GradientAnisotropicDiffusionImageFilter<RealImage,RealImage> ADFilter;
for(int i=0; i<m_segmentationOptions.adfIterations; ++i)
{
ADFilter::Pointer adFilter = ADFilter::New();
adFilter->SetInput(realImage);
adFilter->SetConductanceParameter(1.0);
adFilter->SetNumberOfIterations(1);
adFilter->SetTimeStep(0.0625);
adFilter->Update();
realImage = adFilter->GetOutput();
if(is_aborted()) return;
increment_progress();
}
// Calculate the gradient magnitude of the smoothed image.
typedef itk::GradientMagnitudeImageFilter<RealImage,GradientMagnitudeImage> GMFilter;
GMFilter::Pointer gmFilter = GMFilter::New();
gmFilter->SetInput(realImage);
gmFilter->SetUseImageSpacingOff();
gmFilter->Update();
GradientMagnitudeImage::Pointer gradientMagnitudeImage = gmFilter->GetOutput();
if(is_aborted()) return;
increment_progress();
//~~~~~~~
// STEP 2
//~~~~~~~
set_status("Running watershed...");
// Run the watershed algorithm on the gradient magnitude image.
typedef MeijsterRoerdinkWatershed<GradientMagnitudeImage::PixelType,3> WS;
WS ws(gradientMagnitudeImage, ITKImageUtil::make_6_connected_offsets());
if(is_aborted()) return;
increment_progress();
//~~~~~~~
// STEP 3
//~~~~~~~
set_status("Creating initial partition forest...");
boost::shared_ptr<CTImageLeafLayer> leafLayer(new CTImageLeafLayer(hounsfieldImage, windowedImage, gradientMagnitudeImage));
if(is_aborted()) return;
boost::shared_ptr<CTImageBranchLayer> lowestBranchLayer = IPF::make_lowest_branch_layer(leafLayer, ws.calculate_groups());
if(is_aborted()) return;
m_ipf.reset(new IPF(leafLayer, lowestBranchLayer));
increment_progress();
//~~~~~~~
// STEP 4
//~~~~~~~
set_status("Creating rooted MST for lowest branch layer...");
RootedMST<int> mst(*lowestBranchLayer);
if(is_aborted()) return;
increment_progress();
//~~~~~~~
// STEP 5
//~~~~~~~
set_status("Running waterfall...");
// Iteratively run a Nicholls waterfall pass on the MST until the forest is built.
typedef WaterfallPass<int>::Listener WaterfallPassListener;
NichollsWaterfallPass<int> waterfallPass;
boost::shared_ptr<WaterfallPassListener> listener = make_forest_building_waterfall_pass_listener(m_ipf);
waterfallPass.add_listener(listener);
while(mst.node_count() != 1 && m_ipf->highest_layer() < m_segmentationOptions.waterfallLayerLimit)
{
m_ipf->clone_layer(m_ipf->highest_layer());
if(is_aborted()) return;
waterfallPass.run(mst);
if(is_aborted()) return;
}
set_finished();
}
void forest_test()
{
// Create the Hounsfield and 'windowed' images.
typedef itk::Image<int,2> HounsfieldImage;
typedef itk::Image<unsigned char,2> WindowedImage;
typedef itk::Image<short,2> GradientMagnitudeImage;
int pixels[] =
{
2,2,2,9,9,3,3,3,
2,2,2,9,9,3,3,3,
2,2,2,9,9,3,3,3,
9,9,9,9,9,9,9,9,
9,9,9,9,9,9,9,9,
5,5,5,9,9,3,3,3,
5,5,5,9,9,3,3,3,
5,5,5,9,9,3,3,3,
};
HounsfieldImage::Pointer hounsfieldImage = ITKImageUtil::make_filled_image(8, 8, pixels);
ITKImageUtil::output_image(std::cout, hounsfieldImage);
std::cout << '\n';
// This is not the proper way to do windowing - it's a hack for testing purposes.
typedef itk::CastImageFilter<HounsfieldImage,WindowedImage> CastFilter;
CastFilter::Pointer castFilter = CastFilter::New();
castFilter->SetInput(hounsfieldImage);
castFilter->Update();
WindowedImage::Pointer windowedImage = castFilter->GetOutput();
// Calculate the gradient magnitude image.
typedef itk::GradientMagnitudeImageFilter<WindowedImage,GradientMagnitudeImage> GradientMagnitudeFilter;
GradientMagnitudeFilter::Pointer gradientMagnitudeFilter = GradientMagnitudeFilter::New();
gradientMagnitudeFilter->SetInput(windowedImage);
gradientMagnitudeFilter->SetUseImageSpacingOff();
gradientMagnitudeFilter->Update();
GradientMagnitudeImage::Pointer gradientMagnitudeImage = gradientMagnitudeFilter->GetOutput();
ITKImageUtil::output_image(std::cout, gradientMagnitudeImage);
std::cout << '\n';
// Run the watershed algorithm on the gradient magnitude image.
typedef MeijsterRoerdinkWatershed<GradientMagnitudeImage::PixelType,2> WS;
WS ws(gradientMagnitudeImage, ITKImageUtil::make_4_connected_offsets());
// Output the results.
ITKImageUtil::output_image(std::cout, ws.lower_complete());
std::cout << '\n';
ITKImageUtil::output_image(std::cout, ws.arrows());
std::cout << '\n';
ITKImageUtil::output_image(std::cout, ws.labels());
std::cout << '\n';
// Create the initial partition forest.
typedef PartitionForest<CTImageLeafLayer,CTImageBranchLayer> IPF;
typedef shared_ptr<IPF> IPF_Ptr;
shared_ptr<CTImageLeafLayer> leafLayer(new CTImageLeafLayer(hounsfieldImage, windowedImage, gradientMagnitudeImage));
shared_ptr<CTImageBranchLayer> lowestBranchLayer = IPF::make_lowest_branch_layer(leafLayer, ws.calculate_groups());
std::copy(lowestBranchLayer->edges_cbegin(), lowestBranchLayer->edges_cend(), std::ostream_iterator<WeightedEdge<int> >(std::cout, " "));
std::cout << '\n';
IPF_Ptr ipf(new IPF(leafLayer, lowestBranchLayer));
}
//#################### PUBLIC METHODS ####################
void CTLowestLayersBuilder::execute()
{
typedef itk::Image<short,3> GradientMagnitudeImage;
typedef itk::Image<int,3> HounsfieldImage;
typedef itk::Image<float,3> RealImage;
typedef itk::Image<unsigned char,3> WindowedImage;
HounsfieldImage::Pointer hounsfieldImage = (*m_volume)->base_image();
//~~~~~~~
// STEP 1
//~~~~~~~
set_status("Preprocessing image...");
// Construct the windowed image.
WindowedImage::Pointer windowedImage = (*m_volume)->windowed_image(m_segmentationOptions.windowSettings);
if(is_aborted()) return;
// Cast the input image (whether Hounsfield or windowed) to make its pixels real-valued.
RealImage::Pointer realImage;
switch(m_segmentationOptions.inputType)
{
case CTSegmentationOptions::INPUTTYPE_HOUNSFIELD:
{
typedef itk::CastImageFilter<HounsfieldImage,RealImage> CastFilter;
CastFilter::Pointer castFilter = CastFilter::New();
castFilter->SetInput(hounsfieldImage);
castFilter->Update();
realImage = castFilter->GetOutput();
break;
}
case CTSegmentationOptions::INPUTTYPE_WINDOWED:
{
typedef itk::CastImageFilter<WindowedImage,RealImage> CastFilter;
CastFilter::Pointer castFilter = CastFilter::New();
castFilter->SetInput(windowedImage);
castFilter->Update();
realImage = castFilter->GetOutput();
break;
}
default:
{
throw Exception("Unknown CT segmentation input type"); // this should never happen
}
}
if(is_aborted()) return;
// Smooth this real image using anisotropic diffusion filtering.
typedef itk::GradientAnisotropicDiffusionImageFilter<RealImage,RealImage> ADFilter;
for(int i=0; i<m_segmentationOptions.adfIterations; ++i)
{
ADFilter::Pointer adFilter = ADFilter::New();
adFilter->SetInput(realImage);
adFilter->SetConductanceParameter(1.0);
adFilter->SetNumberOfIterations(1);
adFilter->SetTimeStep(0.0625);
adFilter->Update();
realImage = adFilter->GetOutput();
if(is_aborted()) return;
increment_progress();
}
// Calculate the gradient magnitude of the smoothed image.
typedef itk::GradientMagnitudeImageFilter<RealImage,GradientMagnitudeImage> GMFilter;
GMFilter::Pointer gmFilter = GMFilter::New();
gmFilter->SetInput(realImage);
gmFilter->SetUseImageSpacingOff();
gmFilter->Update();
GradientMagnitudeImage::Pointer gradientMagnitudeImage = gmFilter->GetOutput();
if(is_aborted()) return;
increment_progress();
//~~~~~~~
// STEP 2
//~~~~~~~
set_status("Running watershed...");
// Run the watershed algorithm on the gradient magnitude image.
typedef MeijsterRoerdinkWatershed<GradientMagnitudeImage::PixelType,3> WS;
WS ws(gradientMagnitudeImage, ITKImageUtil::make_6_connected_offsets());
if(is_aborted()) return;
increment_progress();
//~~~~~~~
// STEP 3
//~~~~~~~
set_status("Creating lowest forest layers...");
m_leafLayer.reset(new CTImageLeafLayer(hounsfieldImage, windowedImage, gradientMagnitudeImage));
if(is_aborted()) return;
m_lowestBranchLayer = IPF::make_lowest_branch_layer(m_leafLayer, ws.calculate_groups());
if(is_aborted()) return;
set_finished();
}