ITK-居中变换（配准）

其他 2020-04-15 12:29:02 阅读次数: 0 
#include "itkImageRegistrationMethod.h"
#include "itkMeanSquaresImageToImageMetric.h"
#include "itkRegularStepGradientDescentOptimizer.h"
#include "itkCenteredRigid2DTransform.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkResampleImageFilter.h"
#include "itkSubtractImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkCommand.h"
class CommandIterationUpdate : public itk::Command {
  public:
    typedef  CommandIterationUpdate   Self;
    typedef  itk::Command             Superclass;
    typedef itk::SmartPointer<Self>   Pointer;
    itkNewMacro(Self);

  protected:
    CommandIterationUpdate() {};

  public:
    typedef itk::RegularStepGradientDescentOptimizer  OptimizerType;
    typedef   const OptimizerType                    *OptimizerPointer;

    void Execute(itk::Object *caller, const itk::EventObject &event) ITK_OVERRIDE {
        Execute((const itk::Object *)caller, event);
    }

    void Execute(const itk::Object *object, const itk::EventObject &event) ITK_OVERRIDE {
        OptimizerPointer optimizer = static_cast< OptimizerPointer >(object);
        if(! itk::IterationEvent().CheckEvent(&event)) {
            return;
        }
        std::cout << optimizer->GetCurrentIteration() << "   ";
        std::cout << optimizer->GetValue() << "   ";
        std::cout << optimizer->GetCurrentPosition() << std::endl;
    }
};

int main(int argc, char *argv[]) {
    //实例化图像类
    const    unsigned int    Dimension = 2;
    typedef  unsigned char   PixelType;

    typedef itk::Image< PixelType, Dimension >  FixedImageType;
    typedef itk::Image< PixelType, Dimension >  MovingImageType;

    typedef itk::CenteredRigid2DTransform< double > TransformType;
    typedef itk::RegularStepGradientDescentOptimizer       OptimizerType;
    typedef itk::MeanSquaresImageToImageMetric <
    FixedImageType,
    MovingImageType >    MetricType;
    typedef itk:: LinearInterpolateImageFunction <
    MovingImageType,
    double          >    InterpolatorType;
    typedef itk::ImageRegistrationMethod <
    FixedImageType,
    MovingImageType >    RegistrationType;
//创建配准所需的对象
    MetricType::Pointer         metric        = MetricType::New();
    OptimizerType::Pointer      optimizer     = OptimizerType::New();
    InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
    RegistrationType::Pointer   registration  = RegistrationType::New();
//实例化配准对象
    registration->SetMetric(metric);
    registration->SetOptimizer(optimizer);
    registration->SetInterpolator(interpolator);
    TransformType::Pointer  transform = TransformType::New();
    registration->SetTransform(transform);
//实例化数据源，两个输入图像
    typedef itk::ImageFileReader< FixedImageType  > FixedImageReaderType;
    typedef itk::ImageFileReader< MovingImageType > MovingImageReaderType;

    FixedImageReaderType::Pointer  fixedImageReader  = FixedImageReaderType::New();
    MovingImageReaderType::Pointer movingImageReader = MovingImageReaderType::New();

    fixedImageReader->SetFileName("../data/BrainT1SliceBorder20.png");
    movingImageReader->SetFileName("../data/BrainProtonDensitySliceR10X13Y17.png");

//设置待配准图像和参考图像
    registration->SetFixedImage(fixedImageReader->GetOutput());
    registration->SetMovingImage(movingImageReader->GetOutput());
    fixedImageReader->Update();
    registration->SetFixedImageRegion(
        fixedImageReader->GetOutput()->GetBufferedRegion());
    fixedImageReader->Update();
    movingImageReader->Update();

//计算参考图像的中心，以及得到最大有效区域
    typedef FixedImageType::SpacingType    SpacingType;
    typedef FixedImageType::PointType      OriginType;
    typedef FixedImageType::RegionType     RegionType;
    typedef FixedImageType::SizeType       SizeType;
    FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
    const SpacingType fixedSpacing = fixedImage->GetSpacing();
    const OriginType  fixedOrigin  = fixedImage->GetOrigin();
    const RegionType  fixedRegion  = fixedImage->GetLargestPossibleRegion();
    const SizeType    fixedSize    = fixedRegion.GetSize();
    TransformType::InputPointType centerFixed;
    centerFixed[0] = fixedOrigin[0] + fixedSpacing[0] * fixedSize[0] / 2.0;
    centerFixed[1] = fixedOrigin[1] + fixedSpacing[1] * fixedSize[1] / 2.0;
//计算待配准图像的中心
    MovingImageType::Pointer movingImage = movingImageReader->GetOutput();
    const SpacingType movingSpacing = movingImage->GetSpacing();
    const OriginType  movingOrigin  = movingImage->GetOrigin();
    const RegionType  movingRegion  = movingImage->GetLargestPossibleRegion();
    const SizeType    movingSize    = movingRegion.GetSize();
    TransformType::InputPointType centerMoving;
    centerMoving[0] = movingOrigin[0] + movingSpacing[0] * movingSize[0] / 2.0;
    centerMoving[1] = movingOrigin[1] + movingSpacing[1] * movingSize[1] / 2.0;

    transform->SetCenter(centerFixed);//以参考图像中心为旋转中心
    //以两幅图像重心相减的结果作为变换参数
    transform->SetTranslation(centerMoving - centerFixed);
    transform->SetAngle(0.0);
//初始化配准的变换参数
    registration->SetInitialTransformParameters(transform->GetParameters());
//设置优化器在旋转过程中的缩放比例以及迭代过程中的初始步长，收敛不长，迭代次数等
    typedef OptimizerType::ScalesType       OptimizerScalesType;
    OptimizerScalesType optimizerScales(transform->GetNumberOfParameters());
    const double translationScale = 1.0 / 1000.0;
    optimizerScales[0] = 1.0;
    optimizerScales[1] = translationScale;
    optimizerScales[2] = translationScale;
    optimizerScales[3] = translationScale;
    optimizerScales[4] = translationScale;
    optimizer->SetScales(optimizerScales);
    double initialStepLength = 0.1;
    optimizer->SetRelaxationFactor(0.6);
    optimizer->SetMaximumStepLength(initialStepLength);
    optimizer->SetMinimumStepLength(0.001);
    optimizer->SetNumberOfIterations(200);

    //创建优化器的监听器，并跟踪迭代过程
    CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
    optimizer->AddObserver(itk::IterationEvent(), observer);

    try {
        registration->Update();//触发配准操作
        std::cout << "Optimizer stop condition: "
                  << registration->GetOptimizer()->GetStopConditionDescription()
                  << std::endl;
    } catch(itk::ExceptionObject &err) {
        std::cerr << "ExceptionObject caught !" << std::endl;
        std::cerr << err << std::endl;
        return EXIT_FAILURE;
    }
//获取并打印配准结果参数
    OptimizerType::ParametersType finalParameters =
        registration->GetLastTransformParameters();
    const double finalAngle           = finalParameters[0];
    const double finalRotationCenterX = finalParameters[1];
    const double finalRotationCenterY = finalParameters[2];
    const double finalTranslationX    = finalParameters[3];
    const double finalTranslationY    = finalParameters[4];
    const unsigned int numberOfIterations = optimizer->GetCurrentIteration();
    const double bestValue = optimizer->GetValue();
    // Print out results
    const double finalAngleInDegrees = finalAngle * 180.0 / itk::Math::pi;
    std::cout << "Result = " << std::endl;
    std::cout << " Angle (radians)   = " << finalAngle  << std::endl;
    std::cout << " Angle (degrees)   = " << finalAngleInDegrees  << std::endl;
    std::cout << " Center X      = " << finalRotationCenterX  << std::endl;
    std::cout << " Center Y      = " << finalRotationCenterY  << std::endl;
    std::cout << " Translation X = " << finalTranslationX  << std::endl;
    std::cout << " Translation Y = " << finalTranslationY  << std::endl;
    std::cout << " Iterations    = " << numberOfIterations << std::endl;
    std::cout << " Metric value  = " << bestValue          << std::endl;
//对待配准图像进行重采样操作。以参考图像的参数为输出图像的参数
    typedef itk::ResampleImageFilter <
    MovingImageType,
    FixedImageType >    ResampleFilterType;

    TransformType::Pointer finalTransform = TransformType::New();

    finalTransform->SetParameters(finalParameters);
    finalTransform->SetFixedParameters(transform->GetFixedParameters());

    ResampleFilterType::Pointer resample = ResampleFilterType::New();

    resample->SetTransform(finalTransform);
    resample->SetInput(movingImageReader->GetOutput());

    resample->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
    resample->SetOutputOrigin(fixedImage->GetOrigin());
    resample->SetOutputSpacing(fixedImage->GetSpacing());
    resample->SetOutputDirection(fixedImage->GetDirection());
    resample->SetDefaultPixelValue(100);
//创建数据槽，将重采样结果保存
    typedef itk::ImageFileWriter< FixedImageType >  WriterFixedType;
    WriterFixedType::Pointer      writer =  WriterFixedType::New();
    writer->SetFileName("../save/resample.png");
    writer->SetInput(resample->GetOutput());
    try {
        writer->Update();
    } catch(itk::ExceptionObject &excp) {
        std::cerr << "ExceptionObject while writing the resampled image !" << std::endl;
        std::cerr << excp << std::endl;
        return EXIT_FAILURE;
    }
//利用图像相减滤波器获取参数图像和重采样变换后的待配准原图像见得差异
    typedef itk::Image< float, Dimension > DifferenceImageType;
    typedef itk::SubtractImageFilter <
    FixedImageType,
    FixedImageType,
    DifferenceImageType > DifferenceFilterType;
    DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
    typedef  unsigned char  OutputPixelType;
    typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
    //利用像素强度映射滤波器，将输入图像像素映射到输出图像范围
    typedef itk::RescaleIntensityImageFilter <
    DifferenceImageType,
    OutputImageType >   RescalerType;
    RescalerType::Pointer intensityRescaler = RescalerType::New();
    intensityRescaler->SetOutputMinimum(0);
    intensityRescaler->SetOutputMaximum(255);

    difference->SetInput1(fixedImageReader->GetOutput());
    difference->SetInput2(resample->GetOutput());

    resample->SetDefaultPixelValue(1);

    intensityRescaler->SetInput(difference->GetOutput());
//创建数据草
    typedef itk::ImageFileWriter< OutputImageType >  WriterType;
    WriterType::Pointer      writer2 =  WriterType::New();
    writer2->SetInput(intensityRescaler->GetOutput());
    try {
        //配准后的参考图像和待配准图间的差异写入
        writer2->SetFileName("../save/after.png");
        writer2->Update();
       //配准前的参考图像和待配准图间的差异写入
        TransformType::Pointer identityTransform = TransformType::New();
        identityTransform->SetIdentity();
        resample->SetTransform(identityTransform);
        writer2->SetFileName("../save/before.png");
        writer2->Update();

    } catch(itk::ExceptionObject &excp) {
        std::cerr << "Error while writing difference images" << std::endl;
        std::cerr << excp << std::endl;
        return EXIT_FAILURE;
    }
//
    return EXIT_SUCCESS;
}
呐_糖糖都给你
发布了52 篇原创文章 · 获赞 6 · 访问量 5132
私信 关注

猜你喜欢

转载自blog.csdn.net/weixin_44723106/article/details/105531645
今日推荐
NetBSD 禁止提交由 AI 生成的代码
Apache Doris 2.0.10 版本正式发布！
开源日报 | 大模型开战；大模型独角兽被曝卖身；周鸿祎建议谷歌开源所有产品；最大开源AI社区提供1000万美元共享GPU
开源日报 | Chrome内置Gemini的意义不在于Gemini；中国AI追随之路的五大误区；ECharts创始人“下海”养鱼；谷歌I/O开发者大会什么都有，只是没有惊喜
微软回应中国区AI团队“打包赴美”传闻
基于大语言模型的开源知识库问答系统 MaxKB GitHub Star 数量突破 5,000 个！
周排行
女程序员是这样被恶搞的
B/S 和 C/S 的优缺点
vector一直申请会怎样？
座头鲸识别比赛(Humpback Whale Identification)总结
Linux高性能服务器编程——I/O复用 select
Mysql连接数据库（当包使用）
通过URI获取的文件路径为null的解决方法
1022-Primes on Interval(素数筛选+二分查找) ZCMU
Python出现： TypeError: expected string or buffer
bzoj2434: [Noi2011]阿狸的打字机 ac自动机+树状数组
每日归档
更多
2024-05-18(4)
2024-05-17(34)
2024-05-16(6)
2024-05-15(24)
2024-05-14(0)
2024-05-13(18)
2024-05-12(0)
2024-05-11(38)
2024-05-10(38)
2024-05-09(35)

代码天地 © 2018 codetd.com

境外服务器   最新电视剧2022