ITK配准

void ITKTestClass::ImageRegistration1(std::string fixedImageFile, std::string movingImageFile, std::string outputImagefile, std::string differenceImageAfter, std::string  differenceImageBefore, double initialStepLength)
{
	
	itk::PNGImageIOFactory::RegisterOneFactory();
	itk::TIFFImageIOFactory::RegisterOneFactory();
	itk::BMPImageIOFactory::RegisterOneFactory();
	itk::GDCMImageIOFactory::RegisterOneFactory();
	itk::JPEGImageIOFactory::RegisterOneFactory();

	//1.定义待配准图像类型: 维数, 像素类型
	const    unsigned int    Dimension = 2;
	typedef  unsigned char   PixelType;
	typedef itk::Image< PixelType, Dimension >  FixedImageType;
	typedef itk::Image< PixelType, Dimension >  MovingImageType;

	//2.定义配准框架的基本组件: 变换, 优化, 测度, 插值, 配准组件
	//用于 2D 图像的一个刚体变换,该变换的唯一参数为: 空间座标的类型
	typedef itk::CenteredRigid2DTransform< double > TransformType;
	typedef itk::RegularStepGradientDescentOptimizer       OptimizerType;
	typedef itk::MeanSquaresImageToImageMetric< FixedImageType,
		MovingImageType > MetricType;
	typedef itk::LinearInterpolateImageFunction< MovingImageType,
		double >  InterpolatorType;

	//配准组件: 该组件用于连接其它各组件
	typedef itk::ImageRegistrationMethod< FixedImageType,
		MovingImageType > RegistrationType;

	MetricType::Pointer         metric = MetricType::New();
	OptimizerType::Pointer      optimizer = OptimizerType::New();
	InterpolatorType::Pointer   interpolator = InterpolatorType::New();
	RegistrationType::Pointer   registration = RegistrationType::New();

	//3.使用配准组件将：变换, 优化, 测度, 插值 四个基本组件连接至一起
	registration->SetMetric(metric);
	registration->SetOptimizer(optimizer);
	registration->SetInterpolator(interpolator);
	TransformType::Pointer  transform = TransformType::New();
	registration->SetTransform(transform);

	//4.设置待配准图像及变换区域
	//定义待配准两幅输入图像的 reader
	typedef itk::ImageFileReader< FixedImageType  > FixedImageReaderType;
	typedef itk::ImageFileReader< MovingImageType > MovingImageReaderType;

	FixedImageReaderType::Pointer  fixedImageReader
		= FixedImageReaderType::New();
	MovingImageReaderType::Pointer movingImageReader
		= MovingImageReaderType::New();
	fixedImageReader->SetFileName(fixedImageFile);
	movingImageReader->SetFileName(movingImageFile);

	//本例, 固定图像与浮动图像都来自文件
	//需要 itk::ImageRegistrationMethod 从 file readers 的输出获取输入
	registration->SetFixedImage(fixedImageReader->GetOutput());
	registration->SetMovingImage(movingImageReader->GetOutput());
	//更新 readers,确保在初始化变换时图像参数(size,origin,spacing)有效.
	fixedImageReader->Update();

	//可以将配准限制在固定图像的一特定区域,通过将其作为测度(metric)计算的输入.
	//该区域通过方法SetFixedImageRegion() 定义. 通过使用这个特征, 可以避免图像
	//中某些不需要的的对象影响配准输出,或者减少计算时间,本例中使用整幅图像. 
	//此区域通过 BufferedRegion 标识.
	registration->SetFixedImageRegion(
		fixedImageReader->GetOutput()->GetBufferedRegion());

	//更新固定,浮动图像 reader, 使其 origin, size, spacing 有效,
	fixedImageReader->Update();
	movingImageReader->Update();

	//5.设置各组件的参数，主要是变换、优化组件的参数
	//本例使用的为变换为: 先进行一个旋转变换,再进行一平移变换.
	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();

	//本例使用固定图像的中心点做为旋转中心
	//使用固定图与浮动图中心之间距离向量作为平移量
	//首先计算固定图像中心点
	TransformType::InputPointType centerFixed;
	centerFixed[0] = fixedOrigin[0] + fixedSpacing[0] * fixedSize[0] / 2.0;
	centerFixed[1] = fixedOrigin[1] + fixedSpacing[1] * fixedSize[1] / 2.0;

	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();

	//然后计算浮动图像中心点
	TransformType::InputPointType centerMoving;
	centerMoving[0] = movingOrigin[0] + movingSpacing[0] * movingSize[0] / 2.0;
	centerMoving[1] = movingOrigin[1] + movingSpacing[1] * movingSize[1] / 2.0;

	//设置旋转中心
	transform->SetCenter(centerFixed);
	//设置平移量
	transform->SetTranslation(centerMoving - centerFixed);
	//设置初始旋转角度
	transform->SetAngle(0.0);
	//然后将当前的变换参数作为初始参数, 传递给配准过程
	registration->SetInitialTransformParameters(transform->GetParameters());

	//设置优化组件参数.
	//注意: 旋转和变换的"单位刻度" 是不同的,旋转用弧度度量, 平移以毫米度量
	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);

	//本例使用的优化器是 itk::RegularStepGradientDescentOptimizer, 
	//梯度下降法的一种,下面定义优化参数: 
	//relaxation fator, initial step length, minimal step length, number of iterations
	optimizer->SetRelaxationFactor(0.6);				//松驰因子
	optimizer->SetMaximumStepLength(initialStepLength);	//最大步长
	//最小步长和迭代最大次数, 用来限制优化过程迭代的执行
	optimizer->SetMinimumStepLength(0.001);			//最小步长
	optimizer->SetNumberOfIterations(200);			//最大迭代次数,以防止无限次迭代

	/*
	 *应该注意: 配准过程是一个优化的过程, 优化过程每迭代一次,
	 *就与相似性测度进行一次比较
	 *当相似性测度值达到我们设定的预期值,或者达到设定的迭代上限时
	 *就停止迭代,得到最终结果.
	 */

	 //6.实例化一 Command/Observer 对象, 监视配准过程的执行, 并触发配准过程的执行.
	 //实例化一个 Command/Observer 对象, 将其注册为 optimizer 的观察者
	CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
	optimizer->AddObserver(itk::IterationEvent(), observer);
	//取得配准执行完毕的最终参数
	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();
	const double finalAngleInDegrees = finalAngle * 180.0 / vnl_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;

	//7.重采样, 将变换后的浮动图像映射到固定图像空间中,保存配准结果
	//一般情况,最后一步为: 利用最终的变换结果将浮动图像映射到固定图像空间
	//可通过 itk::ResampleImageFilter 完成
	typedef itk::ResampleImageFilter< MovingImageType,
		FixedImageType >  ResampleFilterType;

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

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

	//设置重采样过滤器的相应参数
	ResampleFilterType::Pointer resample = ResampleFilterType::New();

	resample->SetTransform(finalTransform);
	//将浮动图连接至重采样过滤器的输入端
	resample->SetInput(movingImageReader->GetOutput());

	resample->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
	resample->SetOutputOrigin(fixedImage->GetOrigin());
	resample->SetOutputSpacing(fixedImage->GetSpacing());
	resample->SetOutputDirection(fixedImage->GetDirection());
	//设置默认灰度值,用来 "突出" 显示映射后在浮动图像之外的区域
	resample->SetDefaultPixelValue(100);

	//保存配准后图像
	typedef itk::ImageFileWriter< FixedImageType >  WriterFixedType;
	WriterFixedType::Pointer      writer = WriterFixedType::New();
	writer->SetFileName(outputImagefile);
	writer->SetInput(resample->GetOutput());    //重采样过滤器的输出

	try
	{
		writer->Update();
	}
	catch (itk::ExceptionObject & excp)
	{
		std::cerr << "ExceptionObject while writing the resampled image !" << std::endl;
		std::cerr << excp << std::endl;
	}

	//通过 itk::SubtractImageFilter 比较"经过变换的浮动图像"与"固定图像"之间的差异
	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;

	//由于两幅图像之间的差异可能对应非常小的亮度值
	//使用 itk::RescaleIntensityImageFilter 重新调节亮度值,使其更加明显
	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());

	typedef itk::ImageFileWriter< OutputImageType >  WriterType;
	WriterType::Pointer      writer2 = WriterType::New();
	writer2->SetInput(intensityRescaler->GetOutput());

	try
	{
 
			//保存配准后, 浮动图与固定图之间差异的对比
			writer2->SetFileName(differenceImageAfter);
			writer2->Update();
	 

		//保存配准前, 浮动图与固定图之间差异的对比, 这里仍然需要重采样
		//因为浮动图与固定图不一定有相同的 origin, size, spacing. 
		//可以过一个恒等变换(Identity Transform)完成
		TransformType::Pointer identityTransform = TransformType::New();
		identityTransform->SetIdentity();
		resample->SetTransform(identityTransform); 
		writer2->SetFileName(differenceImageBefore);
		writer2->Update();
	 
	}
	catch (itk::ExceptionObject & excp)
	{
		std::cerr << "Error while writing difference images" << std::endl;
		std::cerr << excp << std::endl;
	}
}