SimpleITK  1.3.0.dev466
CSharp/CannySegmentationLevelSetImageFilter.cs
/*=========================================================================
*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
using System;
using System.Globalization;
using PixelId = itk.simple.PixelIDValueEnum;
using SitkImage = itk.simple.Image;
namespace itk.simple.examples
{
public class Program
{
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());
}
}
}