SimpleITK  1.3.0.dev466
ImageRegistrationMethod2/ImageRegistrationMethod2.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 itk.simple;
namespace itk.simple.examples
{
class IterationUpdate : Command
{
private ImageRegistrationMethod m_Method;
public IterationUpdate(ImageRegistrationMethod m)
{
m_Method = m;
}
public override void Execute()
{
VectorDouble pos = m_Method.GetOptimizerPosition();
Console.WriteLine("{0:3} = {1:10.5} : [{2}, {3}]",
m_Method.GetOptimizerIteration(),
m_Method.GetMetricValue(),
pos[0], pos[1]);
}
}
class ImageRegistrationMethod2
{
static void Main(string[] args)
{
if (args.Length < 3)
{
Console.WriteLine("Usage: %s <fixedImageFilter> <movingImageFile> <outputTransformFile>\n", "ImageRegistrationMethod2");
return;
}
ImageFileReader reader = new ImageFileReader();
reader.SetOutputPixelType(PixelIDValueEnum.sitkFloat32);
reader.SetFileName(args[0]);
Image fixedImage = reader.Execute();
fixedImage = SimpleITK.Normalize(fixedImage);
SimpleITK.DiscreteGaussian(fixedImage, 2.0);
reader.SetFileName(args[1]);
Image movingImage = reader.Execute();
movingImage=SimpleITK.Normalize(movingImage);
movingImage = SimpleITK.DiscreteGaussian(movingImage, 2.0);
ImageRegistrationMethod R = new ImageRegistrationMethod();
R.SetMetricAsJointHistogramMutualInformation();
double learningRate = 1;
uint numberOfIterations = 200;
double convergenceMinimumValue = 1e-4;
uint convergenceWindowSize = 5;
R.SetOptimizerAsGradientDescentLineSearch(learningRate,
numberOfIterations,
convergenceMinimumValue,
convergenceWindowSize);
R.SetInitialTransform(new TranslationTransform(fixedImage.GetDimension()));
R.SetInterpolator(InterpolatorEnum.sitkLinear);
IterationUpdate cmd = new IterationUpdate(R);
R.AddCommand(EventEnum.sitkIterationEvent, cmd);
Transform outTx = R.Execute(fixedImage, movingImage);
outTx.WriteTransform(args[2]);
}
}
}