static void Main(string[] args)
{
if (args.Length < 8)
{
Console.WriteLine("Missing Parameters ");
Console.WriteLine("Usage: " + System.AppDomain.CurrentDomain.FriendlyName +
"inputImage initialModel outputImage cannyThreshold " +
"cannyVariance advectionWeight initialModelIsovalue maximumIterations ");
return;
}
string inputFilename = args[0];
string initialModelFilename = args[1];
string outputFilename = args[2];
double cannyThreshold = double.Parse(args[3], CultureInfo.InvariantCulture);
double cannyVariance = double.Parse(args[4], CultureInfo.InvariantCulture);
double advectionWeight = double.Parse(args[5], CultureInfo.InvariantCulture);
double intialModelIsovalue = double.Parse(args[6], CultureInfo.InvariantCulture);
uint maxIterations = uint.Parse(args[7], CultureInfo.InvariantCulture);
// Read input image
SitkImage inputImage = SimpleITK.ReadImage(inputFilename, PixelId.sitkFloat32);
SitkImage initialModel = SimpleITK.ReadImage(initialModelFilename, PixelId.sitkFloat32);
// The input image will be processed with a few iterations of
// feature-preserving diffusion. We create a filter and set the
// appropriate parameters.
GradientAnisotropicDiffusionImageFilter diffusion = new GradientAnisotropicDiffusionImageFilter();
diffusion.SetConductanceParameter(1.0);
diffusion.SetTimeStep(0.125);
diffusion.SetNumberOfIterations(5);
SitkImage diffusedImage = diffusion.Execute(inputImage);
// As with the other ITK level set segmentation filters, the terms of the
// CannySegmentationLevelSetImageFilter level set equation can be
// weighted by scalars. For this application we will modify the relative
// weight of the advection term. The propagation and curvature term weights
// are set to their defaults of 0 and 1, respectively.
CannySegmentationLevelSetImageFilter cannySegmentation = new CannySegmentationLevelSetImageFilter();
cannySegmentation.SetAdvectionScaling(advectionWeight);
cannySegmentation.SetCurvatureScaling(1.0);
cannySegmentation.SetPropagationScaling(0.0);
// The maximum number of iterations is specified from the command line.
// It may not be desirable in some applications to run the filter to
// convergence. Only a few iterations may be required.
cannySegmentation.SetMaximumRMSError(0.01);
cannySegmentation.SetNumberOfIterations(maxIterations);
// There are two important parameters in the
// CannySegmentationLevelSetImageFilter to control the behavior of the
// Canny edge detection. The variance parameter controls the
// amount of Gaussian smoothing on the input image. The threshold
// parameter indicates the lowest allowed value in the output image.
// Thresholding is used to suppress Canny edges whose gradient magnitudes
// fall below a certain value.
cannySegmentation.SetThreshold(cannyThreshold);
cannySegmentation.SetVariance(cannyVariance);
// Finally, it is very important to specify the isovalue of the surface in
// the initial model input image. In a binary image, for example, the
// isosurface is found midway between the foreground and background values.
cannySegmentation.SetIsoSurfaceValue(intialModelIsovalue);
SitkImage output = cannySegmentation.Execute(initialModel, diffusedImage);
BinaryThresholdImageFilter thresholder = new BinaryThresholdImageFilter();
thresholder.SetUpperThreshold(10.0);
thresholder.SetLowerThreshold(0.0);
thresholder.SetOutsideValue(0);
thresholder.SetInsideValue(255);
output = thresholder.Execute(output);
output = SimpleITK.Cast(output, PixelIDValueEnum.sitkUInt8);
SimpleITK.WriteImage(output, outputFilename);
// Print out some useful information
Console.WriteLine("");
Console.WriteLine("Max. no. iterations: {0}", cannySegmentation.GetNumberOfIterations());
Console.WriteLine("Max. RMS error: {0}", cannySegmentation.GetMaximumRMSError());
Console.WriteLine("");
Console.WriteLine("No. elpased iterations: {0}", cannySegmentation.GetElapsedIterations());
Console.WriteLine("RMS change: {0}", cannySegmentation.GetRMSChange());
}
static void Main(string[] args)
{
if (args.Length < 9)
{
Console.WriteLine("Missing Parameters ");
Console.WriteLine("Usage: " + System.AppDomain.CurrentDomain.FriendlyName +
" inputImage outputImage seedX seedY " +
" Sigma SigmoidAlpha SigmoidBeta TimeThreshold");
return;
}
string inputFilename = args[0];
string outputFilename = args[1];
uint[] seedPosition = { Convert.ToUInt32(args[2]), Convert.ToUInt32(args[3]), 0 };
double sigma = double.Parse(args[4], CultureInfo.InvariantCulture);
double alpha = double.Parse(args[5], CultureInfo.InvariantCulture);;
double beta = double.Parse(args[6], CultureInfo.InvariantCulture);
double timeThreshold = double.Parse(args[7], CultureInfo.InvariantCulture);
double stoppingTime = double.Parse(args[8], CultureInfo.InvariantCulture);
// Read input image
SitkImage inputImage = SimpleITK.ReadImage(inputFilename, PixelIDValueEnum.sitkFloat32);
// The input image will be processed with a few iterations of
// feature-preserving diffusion. We create a filter and set the
// appropriate parameters.
CurvatureAnisotropicDiffusionImageFilter smoothing = new CurvatureAnisotropicDiffusionImageFilter();
smoothing.SetTimeStep(0.125);
smoothing.SetNumberOfIterations(5);
smoothing.SetConductanceParameter(9.0);
SitkImage smoothingOutput = smoothing.Execute(inputImage);
SitkImage gradientMagnitudeOutput = SimpleITK.GradientMagnitudeRecursiveGaussian(smoothingOutput, sigma);
SitkImage sigmoidOutput = SimpleITK.Sigmoid(gradientMagnitudeOutput, alpha, beta, 1.0, 0.0);
FastMarchingImageFilter fastMarching = new FastMarchingImageFilter();
//VectorUIntList trialPoints; Add trialPoints into list if using multiple seeds. Here we only use one seedpoint
VectorUInt32 trialPoint = new VectorUInt32(3);
trialPoint.Add(seedPosition[0]);
trialPoint.Add(seedPosition[1]);
trialPoint.Add(seedPosition[2]);
fastMarching.AddTrialPoint(trialPoint);
// Since the front representing the contour will propagate continuously
// over time, it is desirable to stop the process once a certain time has
// been reached. This allows us to save computation time under the
// assumption that the region of interest has already been computed. The
// value for stopping the process is defined with the method
// SetStoppingValue(). In principle, the stopping value should be a
// little bit higher than the threshold value.
fastMarching.SetStoppingValue(stoppingTime);
SitkImage fastmarchingOutput = fastMarching.Execute(sigmoidOutput);
BinaryThresholdImageFilter thresholder = new BinaryThresholdImageFilter();
thresholder.SetLowerThreshold(0.0);
thresholder.SetUpperThreshold(timeThreshold);
thresholder.SetOutsideValue(0);
thresholder.SetInsideValue(255);
SitkImage result = thresholder.Execute(fastmarchingOutput);
SimpleITK.WriteImage(result, outputFilename);
}
public itk.simple.Image SegmetacjaSledziony(itk.simple.Image image)
{
itk.simple.MorphologicalGradientImageFilter gradientFilter = new MorphologicalGradientImageFilter();
gradientFilter.SetKernelType(KernelEnum.sitkCross);
gradientFilter.SetKernelRadius(1);
itk.simple.Image gradientImage = gradientFilter.Execute(image);
itk.simple.SigmoidImageFilter sigmoidFilter = new SigmoidImageFilter();
sigmoidFilter.SetOutputMinimum(0);
sigmoidFilter.SetOutputMaximum(1);
sigmoidFilter.SetAlpha(1.5);
sigmoidFilter.SetBeta(100);
itk.simple.Image sigmoidImage = sigmoidFilter.Execute(gradientImage);
itk.simple.BinaryThresholdImageFilter threshFilter = new BinaryThresholdImageFilter();
threshFilter.SetLowerThreshold(100);
threshFilter.SetUpperThreshold(180);
threshFilter.SetOutsideValue(0);
threshFilter.SetInsideValue(1);
itk.simple.Image threshImage = threshFilter.Execute(image);
itk.simple.CastImageFilter castThresh = new CastImageFilter();
castThresh.SetOutputPixelType(itk.simple.PixelIDValueEnum.sitkInt16);
itk.simple.Image castThreshImage = castThresh.Execute(threshImage);
itk.simple.SubtractImageFilter substractFilter = new SubtractImageFilter();
itk.simple.Image substractImage = substractFilter.Execute(castThreshImage, sigmoidImage);
itk.simple.ThresholdImageFilter threshFilter2 = new ThresholdImageFilter();
threshFilter2.SetLower(0);
threshFilter2.SetUpper(0);
threshFilter2.SetOutsideValue(1);
itk.simple.Image threshImage2 = threshFilter2.Execute(substractImage);
itk.simple.BinaryErodeImageFilter erodeFilter = new BinaryErodeImageFilter();
erodeFilter.SetForegroundValue(1);
erodeFilter.SetBackgroundValue(0);
erodeFilter.SetKernelType(itk.simple.KernelEnum.sitkCross);
erodeFilter.SetKernelRadius(5);
itk.simple.Image erodeImage = erodeFilter.Execute(threshImage2);
itk.simple.ConnectedComponentImageFilter connFilter = new ConnectedComponentImageFilter();
connFilter.SetFullyConnected(true);
itk.simple.Image connImage = connFilter.Execute(erodeImage);
itk.simple.RelabelComponentImageFilter relabelFilter = new RelabelComponentImageFilter();
relabelFilter.SetMinimumObjectSize(5000);
itk.simple.Image relabelImage = relabelFilter.Execute(connImage);
itk.simple.BinaryThresholdImageFilter threshFilter3 = new BinaryThresholdImageFilter();
threshFilter3.SetInsideValue(1);
threshFilter3.SetOutsideValue(0);
threshFilter3.SetLowerThreshold(1);
threshFilter3.SetUpperThreshold(1);
itk.simple.Image threshImage3 = threshFilter3.Execute(relabelImage);
itk.simple.DilateObjectMorphologyImageFilter closeReconstructFilter = new DilateObjectMorphologyImageFilter();
closeReconstructFilter.SetKernelType(itk.simple.KernelEnum.sitkCross);
closeReconstructFilter.SetKernelRadius(5);
itk.simple.Image closeReconstructImage = closeReconstructFilter.Execute(threshImage3);
itk.simple.ImageFileWriter writer = new ImageFileWriter();
writer.SetFileName("SegmentujSledzione.dcm");
writer.Execute(closeReconstructImage);
return closeReconstructImage;
}
public itk.simple.Image SegmetacjaSledziony(itk.simple.Image image)
{
itk.simple.MorphologicalGradientImageFilter gradientFilter = new MorphologicalGradientImageFilter();
gradientFilter.SetKernelType(KernelEnum.sitkCross);
gradientFilter.SetKernelRadius(1);
itk.simple.Image gradientImage = gradientFilter.Execute(image);
itk.simple.SigmoidImageFilter sigmoidFilter = new SigmoidImageFilter();
sigmoidFilter.SetOutputMinimum(0);
sigmoidFilter.SetOutputMaximum(1);
sigmoidFilter.SetAlpha(1.5);
sigmoidFilter.SetBeta(100);
itk.simple.Image sigmoidImage = sigmoidFilter.Execute(gradientImage);
itk.simple.BinaryThresholdImageFilter threshFilter = new BinaryThresholdImageFilter();
threshFilter.SetLowerThreshold(100);
threshFilter.SetUpperThreshold(180);
threshFilter.SetOutsideValue(0);
threshFilter.SetInsideValue(1);
itk.simple.Image threshImage = threshFilter.Execute(image);
itk.simple.CastImageFilter castThresh = new CastImageFilter();
castThresh.SetOutputPixelType(itk.simple.PixelIDValueEnum.sitkInt16);
itk.simple.Image castThreshImage = castThresh.Execute(threshImage);
itk.simple.SubtractImageFilter substractFilter = new SubtractImageFilter();
itk.simple.Image substractImage = substractFilter.Execute(castThreshImage, sigmoidImage);
itk.simple.ThresholdImageFilter threshFilter2 = new ThresholdImageFilter();
threshFilter2.SetLower(0);
threshFilter2.SetUpper(0);
threshFilter2.SetOutsideValue(1);
itk.simple.Image threshImage2 = threshFilter2.Execute(substractImage);
itk.simple.BinaryErodeImageFilter erodeFilter = new BinaryErodeImageFilter();
erodeFilter.SetForegroundValue(1);
erodeFilter.SetBackgroundValue(0);
erodeFilter.SetKernelType(itk.simple.KernelEnum.sitkCross);
erodeFilter.SetKernelRadius(5);
itk.simple.Image erodeImage = erodeFilter.Execute(threshImage2);
itk.simple.ConnectedComponentImageFilter connFilter = new ConnectedComponentImageFilter();
connFilter.SetFullyConnected(true);
itk.simple.Image connImage = connFilter.Execute(erodeImage);
itk.simple.RelabelComponentImageFilter relabelFilter = new RelabelComponentImageFilter();
relabelFilter.SetMinimumObjectSize(5000);
itk.simple.Image relabelImage = relabelFilter.Execute(connImage);
itk.simple.BinaryThresholdImageFilter threshFilter3 = new BinaryThresholdImageFilter();
threshFilter3.SetInsideValue(1);
threshFilter3.SetOutsideValue(0);
threshFilter3.SetLowerThreshold(1);
threshFilter3.SetUpperThreshold(1);
itk.simple.Image threshImage3 = threshFilter3.Execute(relabelImage);
itk.simple.DilateObjectMorphologyImageFilter closeReconstructFilter = new DilateObjectMorphologyImageFilter();
closeReconstructFilter.SetKernelType(itk.simple.KernelEnum.sitkCross);
closeReconstructFilter.SetKernelRadius(5);
itk.simple.Image closeReconstructImage = closeReconstructFilter.Execute(threshImage3);
itk.simple.ImageFileWriter writer = new ImageFileWriter();
writer.SetFileName("SegmentujSledzione.dcm");
writer.Execute(closeReconstructImage);
return(closeReconstructImage);
}
