ITK图像配准介绍.docx

ITK图像配准介绍1.什么叫配准Image registration is the process of determining the spatial transform that maps points from one image to homologous points on a object in the second image.2.配准的框架3TransformIn the Insight Toolkit, itk:Transform objects encapsulate the mapping of points and vectors from an input space to an output space.ITK provides a variety of transforms from simple translation, rotation and scaling to general affine and kernel transforms.下面详细介绍一些主要的变换方法：3.1 Translation Transform3.2 Scale Transform3.3 Euler2DTransform3.4 CenteredRigid2DTransform3.5 Similarity2DTransformcan be seen as a rigid transform combined with an isotropic scaling factor.3.6 AffineTransform3.7 变换的分类4. Interpolators4.1 为什么要插值?4.2 最常用的两种插值方法4.2.1 Nearest Neighbor Interpolation4.2.2 Linear Interpolation5. MetricsIn ITK, itk:ImageToImageMetric objects quantitatively measure how well the transformed moving image fits the fixed image by comparing the gray-scale intensity of the images. These metrics are very flexible and can work with any transform or interpolation method and do not require reduction of the gray-scale images to sparse extracted information such as edges.下面介绍一些常用的Metrics5.1 Mean Squares MetricThe itk:MeanSquaresImageToImageMetric computes the mean squared pixel-wise difference in intensity between image A and B over a user defined region5.2 Normalized Correlation Metric6. OptimizersThe basic input to an optimizer is a cost function object常用的两种方法6.1 Gradient Descent Advances parameters in the direction of the gradient where the step size is governed by a learning rate ( itk:GradientDescentOptimizer).6.2 Regular Step Gradient DescentAdvances parameters in the direction of the gradient where a bipartition scheme is used to compute the step size (itk:RegularStepGradientDescentOptimizer).7.一个简单的例子实现平移配准#include itkImageRegistrationMethod.h#include itkTranslationTransform.h#include itkLinearInterpolateImageFunction.h#include itkMeanSquaresImageToImageMetric.h#include itkRegularStepGradientDescentOptimizer.h#include itkImage.h#include itkImageFileReader.h#include itkImageFileWriter.h#include itkResampleImageFilter.h#include itkCommand.hclass CommandIterationUpdate : public itk:Commandpublic:typedef CommandIterationUpdate Self;typedef itk:Command Superclass;typedef itk:SmartPointer Pointer;itkNewMacro( Self );protected:CommandIterationUpdate() ;public:typedef itk:RegularStepGradientDescentOptimizer OptimizerType;typedef const OptimizerType * OptimizerPointer;void Execute(itk:Object *caller, const itk:EventObject & event)Execute(const itk:Object *)caller,event);void Execute(const itk:Object * object, const itk:EventObject & event)OptimizerPointer optimizer = dynamic_cast ( object );if( ! itk:IterationEvent().CheckEvent( &event)return;std:cout GetCurrentIteration() std:endl;std:cout GetValue() std:endl;std:cout GetCurrentPosition() std:endl;int main()const unsigned int Dimension = 2;typedef unsigned char PixelType;typedef itk:Image FixImageType;typedef itk:Image MoveImageType;typedef itk:TranslationTransform TransformType;typedef itk:LinearInterpolateImageFunction InterpolateType;typedef itk:MeanSquaresImageToImageMetric MetricType;typedef itk:RegularStepGradientDescentOptimizer OptimizerType;typedef itk:ImageRegistrationMethod RegistrationType;TransformType:Pointer transform = TransformType:New();InterpolateType:Pointer interpolator = InterpolateType:New();MetricType:Pointer metric = MetricType:New();OptimizerType:Pointer optimizer = OptimizerType:New();RegistrationType:Pointer registration = RegistrationType:New();registration-SetTransform( transform );registration-SetInterpolator( interpolator );registration-SetMetric( metric );registration-SetOptimizer( optimizer );typedef itk:ImageFileReader FixedImageReaderType;typedef itk:ImageFileReader MoveImageReaderType;FixedImageReaderType:Pointer fixedImageReader = FixedImageReaderType:New();MoveImageReaderType:Pointer moveImageReader = MoveImageReaderType:New();const char* FixedImageRoot = D:picBFixed.png;const char* MoveImageRoot = D:picBMove2.png;fixedImageReader-SetFileName( FixedImageRoot );moveImageReader-SetFileName( MoveImageRoot );registration-SetFixedImage( fixedImageReader-GetOutput();registration-SetMovingImage( moveImageReader-GetOutput();fixedImageReader-Update();registration-SetFixedImageRegion( fixedImageReader-GetOutput()-GetBufferedRegion();typedef RegistrationType:ParametersType ParametersType;ParametersType initialParameters( transform-GetNumberOfParameters() );initialParameters0 = 0.0;initialParameters1 = 0.0;registration-SetInitialTransformParameters ( initialParameters );optimizer-SetMaximumStepLength ( 4.00 );optimizer-SetMinimumStepLength ( 0.05 );optimizer-SetNumberOfIterations( 200 );CommandIterationUpdate:Pointer observer = CommandIterationUpdate:New();optimizer-AddObserver( itk:IterationEvent(), observer );tryregistration-StartRegistration();std:cout Optimizer stop condition: GetOptimizer()-GetStopConditionDescription() std:endl;catch( itk:ExceptionObject &err )std:cerr ExceptionObject caught ! std:endl;std:cerr err GetLastTransformParameters();const double TranslationAlongX = finalParameters0;const double TranslationAlongY = finalParameters1;const unsigned int numberOfIterations = optimizer-GetCurrentIteration();const double bestValue = optimizer-GetValue();std:cout TranslationAlongX = TranslationAlongX std:endl;std:cout TranslationAlongY = TranslationAlongY std:endl;std:cout numberOfIterations = numberOfIterations std:endl;system(pause);typedef itk:ResampleImageFilter ResampleFilterType;ResampleFilterType:Pointer resampler = ResampleFilterType:New();resampler-SetInput( moveImageReader-GetOutput() );resampler-SetTransform( registration-GetOutput()-Get() );FixImageType:Pointer fixedImage = fixedImageReader-GetOutput();resampler-SetSize( fixedImage-GetLargestPossibleRegion().GetSize() );resampler-SetOutputOrigin( fixedImage-GetOrigin() );resampler-SetOutputSpacing( fixedImage-GetSpacing() );resampler-SetOutputDirection( fixedImage-GetDirection() );resampler-SetDefaultPixelValue( 100 );/typedef itk:ImageFileWriter WriterType;typedef itk:Image OutputImageType;typedef itk:ImageFileWriter WriterType;WriterType:Pointer writer = WriterType:New();/CastFilterType:Pointer caster = CastFilterType:New();const char* writeFile = D:picwrite.png;writer-SetFileName( writeFile );/caster-SetInput( resampler-GetOutput() );/writer-SetInput( caster-GetOutput() );writer-SetInput( resampler-GetOutput();trywriter-Update();catch( itk:ExceptionObject &err )std:cerr ExceptionObject caught ! std:endl;std:cerr err std:endl;system(pause);return -1;return 0 ;8. Multi-Resolution RegistrationMulti-Resolution配准方法，可以增强配准的准确性，速度以及鲁棒性。如图所示，它为多次配准。采用金字塔模型，从第一次的粗糙配准到后面越来越细致