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 }
