这篇博客介绍了如何利用ITK库中的贝叶斯分类器进行医学图像的初始化和分类，特别是在脑部图像分割中的应用。首先，通过BayesianClassifierInitializationImageFilter初始化设置类别数，然后使用BayesianClassifierImageFilter进行图像像素分类，对比了这种方法与K-means分类的速度差异。
#include "itkBayesianClassifierInitializationImageFilter.h"
#include "itkBayesianClassifierImageFilter.h"
//Bayesian Segmentation Approach
typedef itk::BayesianClassifierInitializationImageFilter< InputImageType > BayesianInitializerType;
typename BayesianInitializerType::Pointer bayesianInitializer = BayesianInitializerType::New();
bayesianInitializer->SetInput(reader->GetOutput());
bayesianInitializer->SetNumberOfClasses(numClass);// Background, WM, GM and CSF
bayesianInitializer->Update();
typedef float PriorType;
typedef float PosteriorType;
typedef itk::VectorImage< InputPixelType, Dimension > VectorInputImageType;
typedef itk::BayesianClassifierImageFilter< VectorInputImageType, OutputPixelType, PosteriorType, PriorType > ClassifierFilterType;
typename ClassifierFilterType::Pointer bayesClassifier = ClassifierFilterType::New();
bayesClassifier->SetInput(bayesianInitializer->GetOutput());
bayesClassifier->Update();体会：相比kmean分类，比较慢
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