使用Python解决常见格式图像读取nii,dicom,mhd

pip install SimpleITK

import os
import SimpleITK as sitk
import matplotlib.pyplot as plt
from natsort import natsorted
from tqdm import tqdm
import cv2
import numpy as np

mhd_path = './xxx.mhd'  # mhd文件需和同名raw文件放在同一个文件夹
data = sitk.ReadImage(mhd_path)  # 读取mhd文件
# print(data)
spacing = data.GetSpacing()  # 获得spacing大小
img_data = sitk.GetArrayFromImage(data)  # 获得图像矩阵
print(img_data.shape)

# 图像数据转换为npy保存
np.save('img_data.npy', np.array(img_data))

# 将图像转为png格式保存
for i in range(img_data.shape[0]):
    cv2.imwrite('./{}.png'.format(i), img_data[i, :, :])

pip install SimpleITK
pip install pydicom

import os
import SimpleITK as sitk
import matplotlib.pyplot as plt
from natsort import natsorted
from tqdm import tqdm
import cv2
import pydicom
import numpy as np

#调整图像窗宽窗位
def window_transform(ct_array, window_width, window_center, normal=False):
    min_window = float(window_center) - 0.5*float(window_width)
    new_img = (ct_array - min_window) / float(window_width)
    new_img[new_img < 0] = 0
    new_img[new_img > 1] = 1
    if not normal:
        new_img = (new_img * 255).astype('uint8')
    return new_img


img_path = 'xx.dcm'
#此时读取的是所有dicom图像信息
image = pydicom.read_file(img_path)
#获得图像矩阵
image_data = image.pixel_array
#获得dicom中的患者信息、图像信息等
information['PatientID'] = image.PatientID
information['PatientName'] = image.PatientName
information['PatientBirthDate'] = image.PatientBirthDate
information['PatientSex'] = image.PatientSex
window_width = 1000
window_center = 30
image_data = window_transform(image_data, window_width, window_center, normal=False)
cv2.imwrite('./img.png',image_data)

import os
import SimpleITK as sitk
import matplotlib.pyplot as plt
from natsort import natsorted
from tqdm import tqdm
import cv2
import pydicom
import numpy as np

img_path = 'xx.dcm'
#此时读取的是所有dicom图像信息
image = sitk.ReadImage(img_path)
# 转为值为0-255的灰度图
rescalFilt = sitk.RescaleIntensityImageFilter()
rescalFilt.SetOutputMaximum(255)
rescalFilt.SetOutputMinimum(0)
image = rescalFilt.Execute(image)
image_data = sitk.GetArrayFromImage(image_data)
image_data = np.squeeze(dicom_data)
cv2.imwrite('./img.png',image_data)

pip install nibabel

import os 
import numpy as np 
import nibabel as nib
import matplotlib.pyplot as plt
from glob import glob

img_path = './xxx.nii'
image = nib.load(img_path)    
image_data = image.dataobj
for i in range(0, dataobj.shape[2]): 
    cv2.imwrite('./{}.png'.format(i), img_data[i, :, :])