itk中的图像分割算法（五）

来源：互联网发布：ezzy付强知乎编辑：程序博客网时间：2024/05/16 07:31

今天给大家带来的是分割算法中的经典：区域生长。相信看这篇文章的你绝不会对这个词陌生的，so，具体含义本文就不解释了，直接上代码。

itk算法库中提供的类名叫：itkConnectedThresholdImageFilter

 typedef itk::ConnectedThresholdImageFilter<ImageType, ImageType> ConnectedFilterType; ConnectedFilterType::Pointer connectedThreshold = ConnectedFilterType::New (); ConnectedFilterType->SetInput( input_image); ImageType::IndexType  seed0; seed0[0] = 75; seed0[1] = 142; //seed0[2] = 21;//这里可以设置多维的点，具体要看ImageType定义的维度 connectedThreshold ->AddSeed(seed0);//这里可以设置多个seed的，方法就是不停的add connectedThreshold ->SetLower(  800 ); connectedThreshold ->SetUpper( 3000 ); connectedThreshold ->SetReplaceValue(255); connectedThreshold ->Update();output_image = connectedThreshold ->Getoutput();

是不是很简单？嗯，接口就是这样的easy。接下来看类中的具体实现。

template <class TInputImage, class TOutputImage>void ConnectedThresholdImageFilter<TInputImage,TOutputImage>::GenerateData(){  InputImageConstPointer inputImage = this->GetInput();//输入图像  OutputImagePointer outputImage = this->GetOutput();//输出图像  typename InputPixelObjectType::Pointer lowerThreshold=this->GetLowerInput();//最低阈值  typename InputPixelObjectType::Pointer upperThreshold=this->GetUpperInput();//最高阈值  m_Lower = lowerThreshold->Get();  m_Upper = upperThreshold->Get();  // Zero the output  先用0填充输出图像  OutputImageRegionType region =  outputImage->GetRequestedRegion();  outputImage->SetBufferedRegion( region );  outputImage->Allocate();  outputImage->FillBuffer ( NumericTraits<OutputImagePixelType>::Zero );    //先将输入图像二值化  typedef BinaryThresholdImageFunction<InputImageType, double> FunctionType;  typename FunctionType::Pointer function = FunctionType::New();  function->SetInputImage ( inputImage );  function->ThresholdBetween ( m_Lower, m_Upper );  ProgressReporter progress(this, 0, region.GetNumberOfPixels());//这句忽略  if (this->m_Connectivity == FaceConnectivity)//分类一：二维上的4邻域    {    typedef FloodFilledImageFunctionConditionalIterator<OutputImageType, FunctionType> IteratorType;//这里惊现FloodFill,相信一路看前面文章的同学很熟悉了    IteratorType it ( outputImage, function, m_SeedList );    it.GoToBegin();    while( !it.IsAtEnd())      {      it.Set(m_ReplaceValue);      ++it;      progress.CompletedPixel();  // potential exception thrown here      }    }    else if (this->m_Connectivity == FullConnectivity)//分类二：二维上的8邻域    {    // use the fully connected iterator here. The fully connected iterator     // below is a superset of the above. However, it is reported to be 20%    // slower. Hence we use this "if" block to use the old iterator when    // we don't need full connectivity.    typedef ShapedFloodFilledImageFunctionConditionalIterator<OutputImageType, FunctionType> IteratorType;//一个加强版本的FloodFilled    IteratorType it ( outputImage, function, m_SeedList );    it.FullyConnectedOn();    it.GoToBegin();    while( !it.IsAtEnd())      {      it.Set(m_ReplaceValue);      ++it;      progress.CompletedPixel();  // potential exception thrown here      }    }#endif}

代码不要太简单，思路就是先二值化，然后通过seed来定位，再然后floodfilled填充，然后没了~

所以，关键点在floodfilled，如果看到这里不太明白，请细翻翻我之前的文章，谢谢。

珍惜你现在拥有的一切，用真诚的笑容来面对他们，用全部的力量来保护他们，真希望这一刻就是永恒！

参考文献：

1.https://itk.org/Wiki/ITK/Examples/Broken/Images/ConnectedThresholdImageFilter

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