使用python批量读取mhd格式和raw格式医学图像

#导入需要的包
import os
from PIL import Image
import SimpleITK as sitk
import matplotlib.pyplot as plt
from natsort import natsorted
from tqdm import tqdm

#获取文件内的的子文件夹名称
def get_file_name(filename):
    for root, dirs, files in os.walk(filename):
        array = dirs
        if array:
            return array
#创建空目录
def make_dir(save_path):
	if not os.path.isdir(save_path):
		os.makedirs(save_path)

#将一个病人的所有图片整合在一张图打印出来
def plot_ct_scan(scan, num_column=4, jump=1):
    num_slices = len(scan)
    num_row = (num_slices//jump + num_column - 1) // num_column
    f, plots = plt.subplots(num_row, num_column, figsize=(num_column*5, num_row*5))
    for i in range(0, num_row*num_column):
        plot = plots[i % num_column] if num_row == 1 else plots[i // num_column, i % num_column]        
        plot.axis('off')
        if i < num_slices//jump:
            plot.imshow(scan[i*jump], cmap=plt.cm.bone) 

#读取图片并保存
def save_img(data_path,save_path):
    root = os.path.join(os.getcwd(),data_path)
    paths = os.listdir(root)
    print(paths)
    for path in paths:
        if path.find('mhd')>=0:
            data =sitk.ReadImage(os.path.join(root,path))   #读取mhd文件
            #print(data)
    spacing = data.GetSpacing()
    scan = sitk.GetArrayFromImage(data)
    print('scan.shape',scan.shape) #图像大小
    print('spacing: ', spacing)   #间隔
    print('# slice: ', len(scan)) #切片数量
    #plot_ct_scan(scan)
    make_dir(save_path)
    for i in range(scan.shape[0]):
        im = Image.fromarray(scan[i,:,:])   #这里就是提取的图片数据  
        im.save(save_path+'/'+str(i)+'.png')  #保存图片

if __name__ == '__main__':
	data_dir = 'E:/105casesMask_Seg/'   #mhd数据存放位置
	result_dir = 'E:/labels/'   #读取的图片存放位置
	patients_name = get_file_name( data_dir )
	patients_name = natsorted(patients_name )
	#print(data_dir)
	for patient_name in tqdm(patients_name):   #分别保存每个病人数据
	    data_path = data_dir  + patient_name
	    save_path = result_dir + patient_name
	    make_dir(save_path)
	    save_img(data_path,save_path)