Image Registration Method 2¶
If you are not familiar with the SimpleITK registration framework we recommend that you read the registration overview before continuing with the example.
Overview¶
Code¶
// This one header will include all SimpleITK filters and external
// objects.
#include <SimpleITK.h>
#include <iostream>
#include <stdlib.h>
#include <iomanip>
#include <numeric>
namespace sitk = itk::simple;
class IterationUpdate
: public sitk::Command
{
public:
IterationUpdate( const sitk::ImageRegistrationMethod &m)
: m_Method(m)
{}
virtual void Execute( )
{
// use sitk's output operator for std::vector etc..
using sitk::operator<<;
// stash the stream state
std::ios state(NULL);
state.copyfmt(std::cout);
std::cout << std::fixed << std::setfill(' ') << std::setprecision( 5 );
std::cout << std::setw(3) << m_Method.GetOptimizerIteration();
std::cout << " = " << std::setw(7) << m_Method.GetMetricValue();
std::cout << " : " << m_Method.GetOptimizerPosition() << std::endl;
std::cout.copyfmt(state);
}
private:
const sitk::ImageRegistrationMethod &m_Method;
};
int main(int argc, char *argv[])
{
if ( argc < 4 )
{
std::cerr << "Usage: " << argv[0] << " <fixedImageFilter> <movingImageFile> <outputTransformFile>" << std::endl;
return 1;
}
sitk::Image fixed = sitk::ReadImage( argv[1], sitk::sitkFloat32 );
fixed = sitk::Normalize( fixed );
fixed = sitk::DiscreteGaussian( fixed, 2.0 );
sitk::Image moving = sitk::ReadImage( argv[2], sitk::sitkFloat32 );
moving = sitk::Normalize( moving );
moving = sitk::DiscreteGaussian( moving, 2.0);
sitk::ImageRegistrationMethod R;
R.SetMetricAsJointHistogramMutualInformation( );
const double learningRate = 1;
const unsigned int numberOfIterations = 200;
const double convergenceMinimumValue = 1e-4;
const unsigned int convergenceWindowSize=5;
R.SetOptimizerAsGradientDescentLineSearch ( learningRate,
numberOfIterations,
convergenceMinimumValue,
convergenceWindowSize);
R.SetInitialTransform( sitk::TranslationTransform( fixed.GetDimension() ) );
R.SetInterpolator( sitk::sitkLinear );
IterationUpdate cmd(R);
R.AddCommand( sitk::sitkIterationEvent, cmd);
sitk::Transform outTx = R.Execute( fixed, moving );
std::cout << "-------" << std::endl;
std::cout << outTx.ToString() << std::endl;
std::cout << "Optimizer stop condition: " << R.GetOptimizerStopConditionDescription() << std::endl;
std::cout << " Iteration: " << R.GetOptimizerIteration() << std::endl;
std::cout << " Metric value: " << R.GetMetricValue() << std::endl;
sitk::WriteTransform(outTx, argv[3]);
return 0;
}
#!/usr/bin/env python
from __future__ import print_function
from functools import reduce
import SimpleITK as sitk
import sys
import os
def command_iteration(method) :
print("{0:3} = {1:7.5f} : {2}".format(method.GetOptimizerIteration(),
method.GetMetricValue(),
method.GetOptimizerPosition()))
if len ( sys.argv ) < 4:
print( "Usage: {0} <fixedImageFilter> <movingImageFile> <outputTransformFile>".format(sys.argv[0]))
sys.exit ( 1 )
pixelType = sitk.sitkFloat32
fixed = sitk.ReadImage(sys.argv[1], sitk.sitkFloat32)
fixed = sitk.Normalize(fixed)
fixed = sitk.DiscreteGaussian(fixed, 2.0)
moving = sitk.ReadImage(sys.argv[2], sitk.sitkFloat32)
moving = sitk.Normalize(moving)
moving = sitk.DiscreteGaussian(moving, 2.0)
R = sitk.ImageRegistrationMethod()
R.SetMetricAsJointHistogramMutualInformation()
R.SetOptimizerAsGradientDescentLineSearch(learningRate=1.0,
numberOfIterations=200,
convergenceMinimumValue=1e-5,
convergenceWindowSize=5)
R.SetInitialTransform(sitk.TranslationTransform(fixed.GetDimension()))
R.SetInterpolator(sitk.sitkLinear)
R.AddCommand( sitk.sitkIterationEvent, lambda: command_iteration(R) )
outTx = R.Execute(fixed, moving)
print("-------")
print(outTx)
print("Optimizer stop condition: {0}".format(R.GetOptimizerStopConditionDescription()))
print(" Iteration: {0}".format(R.GetOptimizerIteration()))
print(" Metric value: {0}".format(R.GetMetricValue()))
sitk.WriteTransform(outTx, sys.argv[3])
if ( not "SITK_NOSHOW" in os.environ ):
resampler = sitk.ResampleImageFilter()
resampler.SetReferenceImage(fixed);
resampler.SetInterpolator(sitk.sitkLinear)
resampler.SetDefaultPixelValue(1)
resampler.SetTransform(outTx)
out = resampler.Execute(moving)
simg1 = sitk.Cast(sitk.RescaleIntensity(fixed), sitk.sitkUInt8)
simg2 = sitk.Cast(sitk.RescaleIntensity(out), sitk.sitkUInt8)
cimg = sitk.Compose(simg1, simg2, simg1//2.+simg2//2.)
sitk.Show( cimg, "ImageRegistration2 Composition" )
# Run with:
#
# Rscript --vanilla ImageRegistrationMethod2.R fixedImageFilter movingImageFile outputTransformFile
#
library(SimpleITK)
commandIteration <- function(method)
{
msg <- paste(method$GetOptimizerIteration(), "=",
method$GetMetricValue(), ":",
method$GetOptimizerPosition(), "\n" )
cat(msg)
}
args <- commandArgs( TRUE )
if (length(args) != 3) {
stop("3 arguments expected - fixedImageFilter, movingImageFile, outputTransformFile")
}
pixelType <- 'sitkFloat32'
fixed <- ReadImage(args[[1]], pixelType)
fixed <- Normalize(fixed)
fixed <- DiscreteGaussian(fixed, 2.0)
moving <- ReadImage(args[[2]], pixelType)
moving <- Normalize(moving)
moving <- DiscreteGaussian(moving, 2.0)
R <- ImageRegistrationMethod()
R$SetMetricAsJointHistogramMutualInformation()
R$SetOptimizerAsGradientDescentLineSearch(learningRate=1.0,
numberOfIterations=200,
convergenceMinimumValue=1e-5,
convergenceWindowSize=5)
R$SetInitialTransform(TranslationTransform(fixed$GetDimension()))
R$SetInterpolator('sitkLinear')
R$AddCommand('sitkIterationEvent', function() commandIteration(R))
outTx <- R$Execute(fixed, moving)
cat("-------\n")
outTx
cat("Optimizer stop condition:", R$GetOptimizerStopConditionDescription(), '\n')
cat("Iteration:", R$GetOptimizerIteration(), '\n')
cat("Metric value:", R$GetMetricValue(), '\n')
WriteTransform(outTx, args[[3]])