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 < 1)
{
Console.WriteLine("Usage: inputImage outputImage");
return;
}
string inputFilename = args[0];
string outputFilename = args[1];
// Read input image
SitkImage input = SimpleITK.ReadImage(inputFilename);
// Cast so we know the the pixel type
input = SimpleITK.Cast(input, PixelId.sitkFloat32);
// calculate the number of pixels
VectorUInt32 size = input.GetSize();
int len = 1;
for (int dim = 0; dim < input.GetDimension(); dim++)
{
len *= (int)size[dim];
}
IntPtr buffer = input.GetBufferAsFloat();
// There are two ways to access the buffer:
// (1) Access the underlying buffer as a pointer in an "unsafe" block
// (note that in C# "unsafe" simply means that the compiler can not
// perform full type checking), and requires the -unsafe compiler flag
// unsafe {
// float* bufferPtr = (float*)buffer.ToPointer();
// // Now the byte pointer can be accessed as per Brad's email
// // (of course this example is only a 2d single channel image):
// // This is a 1-D array but can be access as a 3-D. Given an
// // image of size [xS,yS,zS], you can access the image at
// // index [x,y,z] as you wish by image[x+y*xS+z*xS*yS],
// // so x is the fastest axis and z is the slowest.
// for (int j = 0; j < size[1]; j++) {
// for (int i = 0; i < size[0]; i++) {
// float pixel = bufferPtr[i + j*size[1]];
// // Do something with pixel here
// }
// }
// }
// (2) Copy the buffer to a "safe" array (i.e. a fully typed array)
// (note that this means memory is duplicated)
float[] bufferAsArray = new float[len]; // Allocates new memory the size of input
Marshal.Copy(buffer, bufferAsArray, 0, len);
double total = 0.0;
for (int j = 0; j < size[1]; j++)
{
for (int i = 0; i < size[0]; i++)
{
float pixel = bufferAsArray[i + j * size[1]];
total += pixel;
}
}
Console.WriteLine("Pixel value total: {0}", total);
// Set buffer of new SimpleITK Image from managed array.
// bufferAsArray could also have come from a bmp,png,etc...
uint width = input.GetWidth();
uint height = input.GetHeight();
SitkImage outImage = new SitkImage(width, height, PixelId.sitkFloat32);
IntPtr outImageBuffer = outImage.GetBufferAsFloat();
Marshal.Copy(bufferAsArray, 0, outImageBuffer, (int)(size[0] * size[1]));
//
// Write out the resulting file
//
outImage = SimpleITK.RescaleIntensity(outImage, 0, 255);
outImage = SimpleITK.Cast(outImage, PixelId.sitkUInt8);
SimpleITK.WriteImage(outImage, outputFilename);
}
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);
}
static void Main(string[] args)
{
if (args.Length < 6)
{
Console.WriteLine("Missing Parameters ");
Console.WriteLine("Usage: " + System.AppDomain.CurrentDomain.FriendlyName +
" inputImage outputImage" +
" lowerThreshold upperThreshold seedX seedY [seed2X seed2Y ... ]");
return;
}
string inputFilename = args[0];
string outputFilename = args[1];
double lowerThreshold = double.Parse(args[2], CultureInfo.InvariantCulture);
double upperThreshold = double.Parse(args[3], CultureInfo.InvariantCulture);
//
// Read the image
//
SitkImage inputImage = SimpleITK.ReadImage(inputFilename, PixelIDValueEnum.sitkFloat32);
//
// Blur using CurvatureFlowImageFilter
//
CurvatureFlowImageFilter blurFilter = new sitk.CurvatureFlowImageFilter();
blurFilter.SetNumberOfIterations(5);
blurFilter.SetTimeStep(0.125);
inputImage = blurFilter.Execute(inputImage);
//
// Set up ConnectedThresholdImageFilter for segmentation
//
ConnectedThresholdImageFilter segmentationFilter = new sitk.ConnectedThresholdImageFilter();
segmentationFilter.SetLower(lowerThreshold);
segmentationFilter.SetUpper(upperThreshold);
segmentationFilter.SetReplaceValue(255);
for (uint i = 4; i + 1 < args.Length; i += 2)
{
VectorUInt32 seed = new VectorUInt32(new uint[] { Convert.ToUInt32(args[i]), Convert.ToUInt32(args[i + 1]), 0 });
segmentationFilter.AddSeed(seed);
Console.WriteLine("Adding a seed at: ");
for (int j = 0; j + 1 < seed.Count; j++)
{
Console.WriteLine(seed[j] + " ");
}
}
SitkImage outImage = segmentationFilter.Execute(inputImage);
//
// Write out the resulting file
//
SimpleITK.WriteImage(outImage, outputFilename);
return;
}