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云海天教程。

原文链接：https://blog.csdn.net/zhimingf/article/details/71404557