ITK系列25_ 区域增长(孤立连接)算法对脑部MHA文件进行三维分割

红太狼 2023-01-22 06:54 19阅读 0赞

### **实例25** **孤立****连接算法对脑部MHA文件****进行三维分割** ###

#include "itkIsolatedConnectedImageFilter.h"
    #include "itkImage.h"
    #include "itkCastImageFilter.h"
    #include "itkCurvatureFlowImageFilter.h"
    #include "itkImageFileReader.h"
    #include "itkImageFileWriter.h"
    
    int main( int argc, char *argv[] )
    {
      /*if( argc < 7 )
        {
        std::cerr << "Missing Parameters " << std::endl;
        std::cerr << "Usage: " << argv[0];
        std::cerr << " inputImage  outputImage seedX1 seedY1";
        std::cerr << " lowerThreshold seedX2 seedY2" << std::endl;
        return EXIT_FAILURE;
        }*/
      //我们使用一个像素类型和一个特殊维来定义图像的类型：
      typedef   float           InternalPixelType;
      const     unsigned int    Dimension = 3;
      typedef itk::Image< InternalPixelType, Dimension >  InternalImageType;
    
      //下面几行是对 IsolatedConnectedImageFilter 进行实例化的代码
      typedef unsigned char                            OutputPixelType;
      typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
      typedef itk::CastImageFilter< InternalImageType, OutputImageType >
                                                       CastingFilterType;
      CastingFilterType::Pointer caster = CastingFilterType::New();
    
      typedef  itk::ImageFileReader< InternalImageType > ReaderType;
      typedef  itk::ImageFileWriter<  OutputImageType  > WriterType;
    
      ReaderType::Pointer reader = ReaderType::New();
      WriterType::Pointer writer = WriterType::New();
    
      reader->SetFileName( "BrainProtonDensity3Slices.mha" );
      writer->SetFileName( "Isolated_baizhi.mha" );
    
    
      typedef itk::CurvatureFlowImageFilter< InternalImageType, InternalImageType >
        CurvatureFlowImageFilterType;
      CurvatureFlowImageFilterType::Pointer smoothing =
                             CurvatureFlowImageFilterType::New();
    
      typedef itk::IsolatedConnectedImageFilter<InternalImageType,
                                           InternalImageType> ConnectedFilterType;
      //使用 New( ) 方式对这个类的一个文件进行结构化
      ConnectedFilterType::Pointer isolatedConnected = ConnectedFilterType::New();
      //现在连接管道
      smoothing->SetInput( reader->GetOutput() );
      isolatedConnected->SetInput( smoothing->GetOutput() );
      caster->SetInput( isolatedConnected->GetOutput() );
      writer->SetInput( caster->GetOutput() );
      /*IsolatedConnectedImageFilter 期望用户指定一个门限和两个种子。在这个例子中，我们
      从命令行得到它们*/
      smoothing->SetNumberOfIterations(4);
      smoothing->SetTimeStep( 0.125 );
    
      InternalImageType::IndexType  indexSeed1;
      //白质种子点
      indexSeed1[0] = atoi( "61" );
      indexSeed1[1] = atoi( "140" );
      indexSeed1[2] = atoi("2");
      //下门限值（要分割的白质的下门限值）
      const InternalPixelType lowerThreshold = atof( "150" );
    
      InternalImageType::IndexType  indexSeed2;
      //灰质种子点
      indexSeed2[0] = atoi( "63" );
      indexSeed2[1] = atoi( "43" );
      indexSeed2[2] = atoi("2");
      /*由于在 ConnectedThresholdImageFilter 中，现在我们就必须指定在区域中能被输出像素所
      接受的亮度值以及至少一个种子点来定义最初的区域*/
      isolatedConnected->SetLower(  lowerThreshold  );
      isolatedConnected->AddSeed1( indexSeed1 );
      isolatedConnected->AddSeed2( indexSeed2 );
    
      isolatedConnected->SetReplaceValue( 255 );
      /*writer 上的 Updata() 方法触发管道的运行。通常在出现错误和抛出异议时, 从一个 try / catch
      模块调用 updata*/
      try
        {
        writer->Update();
        }
      catch( itk::ExceptionObject & excep )
        {
        std::cerr << "Exception caught !" << std::endl;
        std::cerr << excep << std::endl;
        }
      /*这个亮度值允许我们对两个区域进行分割，使用 GetIsolatedValue() 方式可以对区域进行恢复*/
      std::cout << "Isolated Value Found = ";
      std::cout << isolatedConnected->GetIsolatedValue()  << std::endl;
      return EXIT_SUCCESS;
    }

计算得到白质和灰质的孤立值为180：![20201019105408615.png][]

输入三维图像（BrainProtonDensity3Slices.mha）：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyODA5MDkz_size_16_color_FFFFFF_t_70][] ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyODA5MDkz_size_16_color_FFFFFF_t_70 1][] ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyODA5MDkz_size_16_color_FFFFFF_t_70 2][]

**切片1 切片2 切片3**

输出三维**白质**图像（Isolated\_baizhi.mha）：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyODA5MDkz_size_16_color_FFFFFF_t_70 3][] ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyODA5MDkz_size_16_color_FFFFFF_t_70 4][] ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyODA5MDkz_size_16_color_FFFFFF_t_70 5][]

**切片1 切片2 切片3**

输出三维**脑室**图像（Isolated\_naoshi.mha）：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyODA5MDkz_size_16_color_FFFFFF_t_70 6][] ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyODA5MDkz_size_16_color_FFFFFF_t_70 7][] ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyODA5MDkz_size_16_color_FFFFFF_t_70 8][]

**切片1 切片2 切片3**

输出三维**灰质**图像（Isolated\_naoshi.mha）：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyODA5MDkz_size_16_color_FFFFFF_t_70 9][] ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyODA5MDkz_size_16_color_FFFFFF_t_70 10][] ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyODA5MDkz_size_16_color_FFFFFF_t_70 11][]

**切片1 切片2 切片3**

**ITK系列目录：**

**[1 ITK图像数据表达之图像][1 ITK]**

**[2 ITK图像处理之图像滤波][2 ITK]**

**[3 ITK图像处理之图像分割][3 ITK]**

**注：例程配套素材见系列目录**

[20201019105408615.png]: /images/20221020/9f03647e11234011b4d82c5d4753c3e5.png
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyODA5MDkz_size_16_color_FFFFFF_t_70 6]: https://img-blog.csdnimg.cn/20201019110728308.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMyODA5MDkz,size_16,color_FFFFFF,t_70
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[1 ITK]: https://blog.csdn.net/qq_32809093/article/details/108307568
[2 ITK]: https://blog.csdn.net/qq_32809093/article/details/108882952
[3 ITK]: https://blog.csdn.net/qq_32809093/article/details/108327734