ITK 2D图像刚性配准
2024-05-12 19:59:54
开发环境：VS 2022，Windows 10，ITK5.3
使用数据为"D:\Windows\ITK-5.2.1\Examples\Data"下的BrainProtonDensitySliceBorder20.png和BrainProtonDensitySliceR10X13Y17.png
1. 导入所需头文件
#include "itkImageRegistrationMethodv4.h" //包含配准方法
#include "itkTranslationTransform.h" //空间变换
#include "itkMeanSquaresImageToImageMetricv4.h" //损失函数
#include "itkRegularStepGradientDescentOptimizerv4.h"//优化算法
#include "itkImageFileReader.h"//读取图像
#include "itkImageFileWriter.h"//写入结果
#include "itkPNGImageIOFactory.h" //读取PNG图像所需
using namespace std;
2. 实例化所需对象
定义读取图像的维度和像素类型
constexpr unsigned int Dimension = 2;
using PixelType = float;
实例化对象
//参考和浮动图像的类型在实例化的时候需要指定
using FixedImageType = itk::Image<PixelType, Dimension>;
using MovingImageType = itk::Image<PixelType, Dimension>;
// 空间变换类型和维度
using TransformType = itk::TranslationTransform<double, Dimension>;
//优化算法处理数据的类型
using OptimizerType = itk::RegularStepGradientDescentOptimizerv4<double>;
//计算损失函数的对象，需要指定参考和浮动图像
using MetricType =itk::MeanSquaresImageToImageMetricv4<FixedImageType, MovingImageType>;
//配准方法实例化，需要指定参考和浮动图像
using RegistrationType = itk::ImageRegistrationMethodv4<FixedImageType, MovingImageType, TransformType>;
3.创建部分对象
通过New()创建，通过itk::SmartPointer赋值
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
RegistrationType::Pointer registration = RegistrationType::New();
4. 配置配准方法
将声明的部分对象赋值给配准方法（如优化器，损失函数）
registration->SetMetric(metric);
registration->SetOptimizer(optimizer);
5.配置损失函数
//配置之前需要用到插值方法，因此需要实例化和创建参考和浮动图像的插值对象
using FixedLinearInterpolatorType =itk::LinearInterpolateImageFunction<FixedImageType, double>;
using MovingLinearInterpolatorType =itk::LinearInterpolateImageFunction<MovingImageType, double>;
FixedLinearInterpolatorType::Pointer fixedInterpolator =FixedLinearInterpolatorType::New();
MovingLinearInterpolatorType::Pointer movingInterpolator =MovingLinearInterpolatorType::New();
metric->SetFixedInterpolator(fixedInterpolator);
metric->SetMovingInterpolator(movingInterpolator);
6. 读取图像并放入配准方法中
typedef itk::ImageFileReader< FixedImageType > FixedImageReaderType;
typedef itk::ImageFileReader< MovingImageType > MovingImageReaderType;
FixedImageReaderType::Pointer fixedImageReader = FixedImageReaderType::New();
MovingImageReaderType::Pointer movingImageReader = MovingImageReaderType::New();
itk::PNGImageIOFactory::RegisterOneFactory();
fixedImageReader->SetFileName(R"(C:\Users\Richard\Desktop\BrainProtonDensitySliceBorder20.png)");
movingImageReader->SetFileName(R"(C:\Users\Richard\Desktop\BrainProtonDensitySliceR10X13Y17.png)");registration->SetFixedImage(fixedImageReader->GetOutput());
registration->SetMovingImage(movingImageReader->GetOutput());
7.配置空间变换矩阵
TransformType::Pointer movingInitialTransform = TransformType::New();
TransformType::ParametersType initialParameters(movingInitialTransform->GetNumberOfParameters());
initialParameters[0] = 0.0; // Initial offset in mm along X
initialParameters[1] = 0.0; // Initial offset in mm along Y
movingInitialTransform->SetParameters(initialParameters);
registration->SetMovingInitialTransform(movingInitialTransform);
TransformType::Pointer identityTransform = TransformType::New();
identityTransform->SetIdentity();
registration->SetFixedInitialTransform(identityTransform);
8.配置优化器参数
optimizer->SetLearningRate(4);
optimizer->SetMinimumStepLength(0.001);
optimizer->SetRelaxationFactor(0.5);
optimizer->SetNumberOfIterations(200);
constexpr unsigned int numberOfLevels = 1;
RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
shrinkFactorsPerLevel.SetSize(1);
shrinkFactorsPerLevel[0] = 1;
RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
smoothingSigmasPerLevel.SetSize(1);
smoothingSigmasPerLevel[0] = 0;
registration->SetNumberOfLevels(numberOfLevels);
registration->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel);
9. 开始配准并输出最终结果
try{registration->Update();std::cout << "Optimizer stop condition: "<< registration->GetOptimizer()->GetStopConditionDescription()<< std::endl;}catch (const itk::ExceptionObject& err){std::cerr << "ExceptionObject caught !" << std::endl;std::cerr << err << std::endl;return EXIT_FAILURE;}TransformType::ConstPointer transform = registration->GetTransform();TransformType::ParametersType finalParameters = transform->GetParameters();const double TranslationAlongX = finalParameters[0];const double TranslationAlongY = finalParameters[1];const unsigned int numberOfIterations = optimizer->GetCurrentIteration();const double bestValue = optimizer->GetValue();cout << bestValue<<endl;cout << TranslationAlongX << endl;cout << TranslationAlongY << endl;
}
##完整代码
#include "itkImageRegistrationMethodv4.h"
#include "itkTranslationTransform.h"
#include "itkMeanSquaresImageToImageMetricv4.h"
#include "itkRegularStepGradientDescentOptimizerv4.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkPNGImageIOFactory.h"
using namespace std;
int main()
{constexpr unsigned int Dimension = 2;using PixelType = float;using FixedImageType = itk::Image<PixelType, Dimension>;using MovingImageType = itk::Image<PixelType, Dimension>;using TransformType = itk::TranslationTransform<double, Dimension>;using OptimizerType = itk::RegularStepGradientDescentOptimizerv4<double>;using MetricType =itk::MeanSquaresImageToImageMetricv4<FixedImageType, MovingImageType>;using RegistrationType = itk::ImageRegistrationMethodv4<FixedImageType, MovingImageType, TransformType>;MetricType::Pointer metric = MetricType::New();OptimizerType::Pointer optimizer = OptimizerType::New();RegistrationType::Pointer registration = RegistrationType::New();registration->SetMetric(metric);registration->SetOptimizer(optimizer);using FixedLinearInterpolatorType =itk::LinearInterpolateImageFunction<FixedImageType, double>;using MovingLinearInterpolatorType =itk::LinearInterpolateImageFunction<MovingImageType, double>;FixedLinearInterpolatorType::Pointer fixedInterpolator =FixedLinearInterpolatorType::New();MovingLinearInterpolatorType::Pointer movingInterpolator =MovingLinearInterpolatorType::New();metric->SetFixedInterpolator(fixedInterpolator);metric->SetMovingInterpolator(movingInterpolator);typedef itk::ImageFileReader< FixedImageType > FixedImageReaderType;typedef itk::ImageFileReader< MovingImageType > MovingImageReaderType;FixedImageReaderType::Pointer fixedImageReader = FixedImageReaderType::New();MovingImageReaderType::Pointer movingImageReader = MovingImageReaderType::New();itk::PNGImageIOFactory::RegisterOneFactory();fixedImageReader->SetFileName(R"(C:\Users\Richard\Desktop\BrainProtonDensitySliceBorder20.png)");movingImageReader->SetFileName(R"(C:\Users\Richard\Desktop\BrainProtonDensitySliceR10X13Y17.png)");registration->SetFixedImage(fixedImageReader->GetOutput());registration->SetMovingImage(movingImageReader->GetOutput());TransformType::Pointer movingInitialTransform = TransformType::New();TransformType::ParametersType initialParameters(movingInitialTransform->GetNumberOfParameters());initialParameters[0] = 0.0; // Initial offset in mm along XinitialParameters[1] = 0.0; // Initial offset in mm along YmovingInitialTransform->SetParameters(initialParameters);registration->SetMovingInitialTransform(movingInitialTransform);TransformType::Pointer identityTransform = TransformType::New();identityTransform->SetIdentity();registration->SetFixedInitialTransform(identityTransform);optimizer->SetLearningRate(4);optimizer->SetMinimumStepLength(0.001);optimizer->SetRelaxationFactor(0.5);optimizer->SetNumberOfIterations(200);constexpr unsigned int numberOfLevels = 1;RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;shrinkFactorsPerLevel.SetSize(1);shrinkFactorsPerLevel[0] = 1;RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;smoothingSigmasPerLevel.SetSize(1);smoothingSigmasPerLevel[0] = 0;registration->SetNumberOfLevels(numberOfLevels);registration->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel);try{registration->Update();std::cout << "Optimizer stop condition: "<< registration->GetOptimizer()->GetStopConditionDescription()<< std::endl;}catch (const itk::ExceptionObject& err){std::cerr << "ExceptionObject caught !" << std::endl;std::cerr << err << std::endl;return EXIT_FAILURE;}TransformType::ConstPointer transform = registration->GetTransform();TransformType::ParametersType finalParameters = transform->GetParameters();const double TranslationAlongX = finalParameters[0];const double TranslationAlongY = finalParameters[1];const unsigned int numberOfIterations = optimizer->GetCurrentIteration();const double bestValue = optimizer->GetValue();cout << bestValue<<endl;cout << TranslationAlongX << endl;cout << TranslationAlongY << endl;
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