主要实现的部分是利用NameGeneratorType读入系列图像，见头文件#include "itkNumericSeriesFileNames.h"。

需要包含的头文件有：

#include "itkImage.h"
#include "itkImageSeriesReader.h"
#include "itkImageFileWriter.h"
#include "itkNumericSeriesFileNames.h"
#include "itkPNGImageIO.h"//转成JPG格式，将PNG替换成JPEG就可以。

int main( int argc, char ** argv )
{
 // 需要四个参数，分别是程序起点，第一张图像的编号和最后一张图像的变化，输出文件的名称（包含路径）
 if( argc < 4 )
 {
 std::cerr << "Usage: " << std::endl;
 std::cerr << argv[0] << " firstSliceValue lastSliceValue outputImageFile " << std::endl;
 return EXIT_FAILURE;
 }
//定义读入图像类型，创建对应的reader
 typedef unsigned char  PixelType;
 const unsigned int Dimension = 3;

 typedef itk::Image< PixelType, Dimension > ImageType;

 typedef itk::ImageSeriesReader< ImageType > ReaderType;
 typedef itk::ImageFileWriter< ImageType > WriterType;

 ReaderType::Pointer reader = ReaderType::New();
 WriterType::Pointer writer = WriterType::New();

//输入参数定义
 const unsigned int first = atoi( argv[1] );
 const unsigned int last = atoi( argv[2] );
 const char * outputFilename = argv[3];//输出的文件名加上对应格式的后缀即可，如mha或nii.gz

//系列图像读入
 typedef itk::NumericSeriesFileNames NameGeneratorType;
 NameGeneratorType::Pointer nameGenerator = NameGeneratorType::New();
 nameGenerator->SetSeriesFormat( "vwe%03d.png" );

 nameGenerator->SetStartIndex( first );
 nameGenerator->SetEndIndex( last );
 nameGenerator->SetIncrementIndex( 1 );//张数的增长间距

//读入图像，写出图像，进行Update
 reader->SetImageIO( itk::PNGImageIO::New() );
 reader->SetFileNames( nameGenerator->GetFileNames() );
 writer->SetFileName( outputFilename );
 writer->SetInput( reader->GetOutput() );

 try
 {
 writer->Update();
 }
 catch( itk::ExceptionObject & err )
 {
 std::cerr << "ExceptionObject caught !" << std::endl;
 std::cerr << err << std::endl;
 return EXIT_FAILURE;
 }

 return EXIT_SUCCESS;
}

补充知识：将一组png图片转为nii.gz

主要之前使用matlab 对numpy数组存放方式不是很了解.应该是[z,x,y]这样在itksnamp上看就对了

import SimpleITK as sitk
import glob
import numpy as np
from PIL import Image
import cv2

import matplotlib.pyplot as plt # plt 用于显示图片
def save_array_as_nii_volume(data, filename, reference_name = None):
 """
 save a numpy array as nifty image
 inputs:
 data: a numpy array with shape [Depth, Height, Width]
 filename: the ouput file name
 reference_name: file name of the reference image of which affine and header are used
 outputs: None
 """
 img = sitk.GetImageFromArray(data)
 if(reference_name is not None):
 img_ref = sitk.ReadImage(reference_name)
 img.CopyInformation(img_ref)
 sitk.WriteImage(img, filename)

image_path = './oriCvLab/testCvlab/img/'
image_arr = glob.glob(str(image_path) + str("/*"))
image_arr.sort()

print(image_arr, len(image_arr))
allImg = []
allImg = np.zeros([165, 768,1024], dtype='uint8')
for i in range(len(image_arr)):
 single_image_name = image_arr[i]
 img_as_img = Image.open(single_image_name)
 # img_as_img.show()
 img_as_np = np.asarray(img_as_img)
 allImg[i, :, :] = img_as_np

# np.transpose(allImg,[2,0,1])
save_array_as_nii_volume(allImg, './testImg.nii.gz')
print(np.shape(allImg))
img = allImg[:, :, 55]
# plt.imshow(img, cmap='gray')
# plt.show()

以上这篇ITK 实现多张图像转成单个nii.gz或mha文件案例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持python博客。