ITK 实例9 孤立连接算法对脑部MHA文件进行三维分割

 1 #include "itkIsolatedConnectedImageFilter.h"
 2 #include "itkImage.h"
 3 #include "itkCastImageFilter.h"
 4 #include "itkCurvatureFlowImageFilter.h"
 5 #include "itkImageFileReader.h"
 6 #include "itkImageFileWriter.h"
 7  
 8 int main( int argc, char *argv[] )
 9 {
10   /*if( argc < 7 )
11     {
12     std::cerr << "Missing Parameters " << std::endl;
13     std::cerr << "Usage: " << argv[0];
14     std::cerr << " inputImage  outputImage seedX1 seedY1";
15     std::cerr << " lowerThreshold seedX2 seedY2" << std::endl;
16     return EXIT_FAILURE;
17     }*/
18   //我们使用一个像素类型和一个特殊维来定义图像的类型：
19   typedef   float           InternalPixelType;
20   const     unsigned int    Dimension = 3;
21   typedef itk::Image< InternalPixelType, Dimension >  InternalImageType;
22  
23   //下面几行是对 IsolatedConnectedImageFilter 进行实例化的代码
24   typedef unsigned char                            OutputPixelType;
25   typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
26   typedef itk::CastImageFilter< InternalImageType, OutputImageType >
27                                                    CastingFilterType;
28   CastingFilterType::Pointer caster = CastingFilterType::New();
29  
30   typedef  itk::ImageFileReader< InternalImageType > ReaderType;
31   typedef  itk::ImageFileWriter<  OutputImageType  > WriterType;
32  
33   ReaderType::Pointer reader = ReaderType::New();
34   WriterType::Pointer writer = WriterType::New();
35  
36   reader->SetFileName( "BrainProtonDensity3Slices.mha" );
37   writer->SetFileName( "Isolated_baizhi.mha" );
38  
39  
40   typedef itk::CurvatureFlowImageFilter< InternalImageType, InternalImageType >
41     CurvatureFlowImageFilterType;
42   CurvatureFlowImageFilterType::Pointer smoothing =
43                          CurvatureFlowImageFilterType::New();
44  
45   typedef itk::IsolatedConnectedImageFilter<InternalImageType,
46                                        InternalImageType> ConnectedFilterType;
47   //使用 New( ) 方式对这个类的一个文件进行结构化
48   ConnectedFilterType::Pointer isolatedConnected = ConnectedFilterType::New();
49   //现在连接管道
50   smoothing->SetInput( reader->GetOutput() );
51   isolatedConnected->SetInput( smoothing->GetOutput() );
52   caster->SetInput( isolatedConnected->GetOutput() );
53   writer->SetInput( caster->GetOutput() );
54   /*IsolatedConnectedImageFilter 期望用户指定一个门限和两个种子。在这个例子中，我们
55   从命令行得到它们*/
56   smoothing->SetNumberOfIterations(4);
57   smoothing->SetTimeStep( 0.125 );
58  
59   InternalImageType::IndexType  indexSeed1;
60   //白质种子点
61   indexSeed1[0] = atoi( "61" );
62   indexSeed1[1] = atoi( "140" );
63   indexSeed1[2] = atoi("2");
64   //下门限值（要分割的白质的下门限值）
65   const InternalPixelType lowerThreshold = atof( "150" );
66  
67   InternalImageType::IndexType  indexSeed2;
68   //灰质种子点
69   indexSeed2[0] = atoi( "63" );
70   indexSeed2[1] = atoi( "43" );
71   indexSeed2[2] = atoi("2");
72   /*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
73   接受的亮度值以及至少一个种子点来定义最初的区域*/
74   isolatedConnected->SetLower(  lowerThreshold  );
75   isolatedConnected->AddSeed1( indexSeed1 );
76   isolatedConnected->AddSeed2( indexSeed2 );
77  
78   isolatedConnected->SetReplaceValue( 255 );
79   /*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
80   模块调用 updata*/
81   try
82     {
83     writer->Update();
84     }
85   catch( itk::ExceptionObject & excep )
86     {
87     std::cerr << "Exception caught !" << std::endl;
88     std::cerr << excep << std::endl;
89     }
90   /*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*/
91   std::cout << "Isolated Value Found = ";
92   std::cout << isolatedConnected->GetIsolatedValue()  << std::endl;
93   return EXIT_SUCCESS;
94 }