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：
代码语言：javascript复制#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( "vwed.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
代码语言：javascript复制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。
