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 sitkimport globimport numpy as npfrom PIL import Imageimport 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。