思路：深度搜索（dfs）。

接下来我们用C++进行编程：

class Solution {
public:
    /**
     * 代码中的类名、方法名、参数名已经指定，请勿修改，直接返回方法规定的值即可
     * 获取最大连通区域
     * @param data int整型vector<vector<>> 二维数组
     * @return int整型
     */
    int getMaxArea(vector<vector<int> >& data) {
        // write code here
        int ans = 0;
        for (int i = 0; i != data.size(); ++i)
        {
            for (int j = 0; j != data[0].size(); ++j)
                ans = max(ans, dfs(data, i, j));
        }
        return ans;
    }
    
    int dfs(vector<vector<int>>& grid, int cur_i, int cur_j) {
        if (cur_i < 0 || cur_j < 0 || cur_i == grid.size() || cur_j == grid[0].size() || grid[cur_i][cur_j] != 1)
            return 0;
        grid[cur_i][cur_j] = 0;
        int di[4] = {0, 0, 1, -1};
        int dj[4] = {1, -1, 0, 0};
        int ans = 1;
        for (int index = 0; index != 4; ++index) {
            int next_i = cur_i + di[index], next_j = cur_j + dj[index];
            ans += dfs(grid, next_i, next_j);
        }
        return ans;
    }
};

1、代码

#include "itkConnectedComponentImageFilter.h"
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkImageRegionIterator.h"
#include "itkLabelShapeKeepNObjectsImageFilter.h"

using namespace std;

void print(const std::string& file, itk::Image<unsigned char, 2>* image)
{
	ofstream fout(file);
	auto size = image->GetBufferedRegion().GetSize();
	for (auto i = 0; i < size[0]; i++)
	{
		for (auto j = 0; j < size[1]; j++)
		{
			itk::Image<unsigned char, 2>::IndexType index{ {i,j} };
			auto c = image->GetPixel(index);
			fout << (int)c;
		}
		fout << std::endl;
	}
}


int main(int argc, char *argv[])
{
	// 1、获取二值化的分割结果mask
	const char* input_path = "D:/documents/vs2019/itk_tutorial/largestcompent/build/RelWithDebInfo/left_femur.nrrd";// 图像保存的路径
	using ImageType = itk::Image<unsigned char, 3>;
	using ReaderFilter = itk::ImageFileReader<ImageType>;
	auto reader = ReaderFilter::New();
	reader->SetFileName(input_path);
	reader->Update();
	ImageType::Pointer image = reader->GetOutput();

	// 2, 调用ITK的itkConnectedComponentImageFilter
	using ConnectedComponentFilter = itk::ConnectedComponentImageFilter<ImageType, ImageType>;
	auto connnectedComponentFilter = ConnectedComponentFilter::New();
	connnectedComponentFilter->SetInput(image);
	connnectedComponentFilter->Update();

	// 3,调用ITK的itkLabelShapeKeepNObjectsImageFilter
	using LabelShapeKeepNObjectsFilter = itk::LabelShapeKeepNObjectsImageFilter<ImageType>;
	auto labelShapeKeepNObjectsFilter = LabelShapeKeepNObjectsFilter::New();
	labelShapeKeepNObjectsFilter->SetInput(connnectedComponentFilter->GetOutput());
	labelShapeKeepNObjectsFilter->SetBackgroundValue(0);
	labelShapeKeepNObjectsFilter->SetNumberOfObjects(1);
	labelShapeKeepNObjectsFilter->SetAttribute(
		LabelShapeKeepNObjectsFilter::LabelObjectType::NUMBER_OF_PIXELS);
	labelShapeKeepNObjectsFilter->Update();
// 4、将Label便签转换为1
 typedef itk::RescaleIntensityImageFilter<ImageType, ImageType> RescaleFilterType;
  RescaleFilterType::Pointer rescaleFilter = RescaleFilterType::New();
  rescaleFilter->SetOutputMinimum(0);
  rescaleFilter->SetOutputMaximum(1);
  rescaleFilter->SetInput(labelShapeKeepNObjectsFilter->GetOutput());
  rescaleFilter->Update();
  //ImageType::Pointer largestccimage = rescaleFilter->GetOutput(); 

	// 5,保存最大连通区域图像

	const char* out_path = "D:/documents/vs2019/itk_tutorial/largestcompent/build/RelWithDebInfo/output.nii.gz"; // 保存输出结果的路径
	using WriterFilter = itk::ImageFileWriter<ImageType>;
	auto writer = WriterFilter::New();
	writer->SetFileName(out_path);
	writer->SetInput(rescaleFilter->GetOutput());
	writer->Update();
	 
	return EXIT_SUCCESS;
}

2、效果

原始图像：

去除小连通域之后

参考:基于C++和ITK获取2D或3D医学图像的最大连通区域(完整代码)

3、主要函数介绍

itk::ConnectedComponentImageFilter<ImageType, OutputImageType>;

该filter值检测连通区域，不编号，不排序。

itk::LabelShapeKeepNObjectsImageFilter;

该filter对连通区域进行排序，然后只保留最大的几个连通区域。

itk::LabelImageToShapeLabelMapFilter< OutputImageType, LabelMapType> ;

为保留的每个连通区域打上标签（标号），编号顺序为找到不同区域的顺序.

虽然LabelShapeKeepNObjectsImageFilter调用SetAttribute 之后，按照attribute对连通区域大小进行了排序，但该排序并不反映在label上。即大的label并不一定对应大的size。反映在 SetNumberOfObjects(n) 设定保留区域之后，只保留排序后前n个区域的label。

itk::RescaleIntensityImageFilter<OutputImageType, ImageType>；

上一步图像只有标签，图像上没有直接的区别，本步对保留下来的不同连通区域进行不同的染色，以示区分。

参考：二维ITK连通区域提取