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getMhdslices_images.py
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import os
from glob import glob
import cv2
import numpy as np
import nibabel as nib
import SimpleITK as sitk
#读取mhd
def read_mhd(mhd_dir):
import SimpleITK as sitk
# mhd_file = os.path.join(mhd_dir, '24.mhd')
itkimage = sitk.ReadImage(mhd_dir)
ct_value= sitk.GetArrayFromImage(itkimage) # 这里一定要注意，得到的是[z,y,x]格式,
direction = itkimage.GetDirection() # mhd文件中的TransformMatrix
origin = np.array(itkimage.GetOrigin())
spacing = np.array(itkimage.GetSpacing()) # 文件中的ElementSpacing
return ct_value
#设置窗宽、窗位
def set_window(img_data, win_width, win_center):
img_temp = img_data
min = (2 * win_center - win_width) / 2.0 + 0.5
max = (2 * win_center + win_width) / 2.0 + 0.5
dFactor = 255.0 / (max - min)
img_temp = ((img_temp - min) * dFactor).astype(np.int)
img_temp = np.clip(img_temp, 0, 255)
return img_temp
#转换ElementSpacing空间
def resample(image, spacing, new_spacing=[1, 1, 1]):
import scipy
raw_shape = image.shape
resize_factor = spacing / new_spacing
new_real_shape = image.shape * resize_factor
new_shape = np.round(new_real_shape).astype(np.int) # 返回浮点数x的四舍五入值。
real_resize_factor = new_shape / image.shape
new_spacing = spacing / real_resize_factor
image = scipy.ndimage.interpolation.zoom(image, real_resize_factor, mode='nearest') # 使用所请求顺序的样条插值来缩放数组。
#获取CT_VALUE
file_path="E:\\bigorgs\\Coronary\\3D\\CT\\normal\\"
axis_save_path='E:\\bigorgs\\cardiovascular\\workspace\\Medical_tools\\output\\axis\\'
cor_save_path='E:\\bigorgs\\cardiovascular\\workspace\\Medical_tools\\output\\coronal\\'
sag_save_path='E:\\bigorgs\\cardiovascular\\workspace\\Medical_tools\\output\\sagittal\\'
files = glob(file_path + '/*.nii.gz')
for file in files:
ct_value=read_mhd(file)
test=sitk.GetImageFromArray(ct_value) #numpy to cta
# save_path = "E:\\bigorgs\\cardiovascular\\workspace\\Medical_tools\\output"
# sitk.WriteImage(test,os.path.join(save_path, 'test.nii.gz'))
# nib.save(test, os.path.join(save_path, 'test.nii.gz'))
# print(ct_value)
#获取文件名
filename = os.path.basename(file)
#转换CT、ElementSpacing空间
# image = resample(ct_value, spacing[::-1]) # spacing[::-1]是因为ct_value的顺序是[z,y,x]，而读入的spacing是[x,y,z]
#获取图像三个切面图片
ct_value_new = set_window(ct_value,750,90) #窗宽：750；窗位（窗中心）：90
# img = nib.Nifti1Image(ct_value_new, np.eye(4))
# nib.save(img, os.path.join(save_path, 'axis_1.nii.gz'))
# # k取DimSize 中的值
# tar_img = ct_value_new[k, :, :] # 水平面
# cor_img = ct_value_new[:, k, :] # 冠状面
# sag_img = ct_value_new[:, :, k] # 矢状面
a = ct_value.shape[0]
b = ct_value.shape[1]
c = ct_value.shape[2]
print("---------开始转化------")
#水平面
for k in range(ct_value.shape[0]):
# image
# img_axis=ct_value_new[k, :, :]
axis_gray_img = ct_value_new[k, :, :].astype(np.uint8)
# axis_gray_img = ct_value[k, :, :].astype(np.uint8)
# label
# axis_gray_img = ct_value[k, :, :].astype(np.uint8)
#标签图片需要*255
# axis_gray_img=axis_gray_img *255
#image-jpg,label-png
cv2.imwrite(axis_save_path+filename[:-7] + '_'+str(k+1)+'.png', axis_gray_img )
#nii
# axis_gray_img = np.rot90(axis_gray_img, k=3)
# img = nib.Nifti1Image(axis_gray_img, np.eye(4))
# nib.save(img, 'E:\\bigorgs\\medical_data\\Conary\\Hospital\\dongbei\\2D\\images\\axis\\' + '1_' + str(k + 1) + ".nii") # 保存 nii 文件
# nib.save(img, axis_save_path+filename[:-7] + '_'+str(k+1)+'.nii') # 保存 nii 文件
print("---------水平面转化完成------")
#冠状面
for k in range(ct_value.shape[1]):
# image
# cor_gray_img = ct_value[:, k, :].astype(np.uint8)
cor_gray_img = ct_value_new[:, k, :].astype(np.uint8)
# label
# cor_gray_img = ct_value[:, k, :].astype(np.uint8)
# png
# sag_gray_img = np.fliplr(sag_gray_img) # 左右翻转
cor_gray_img=np.flipud(cor_gray_img) #上下翻转
# cor_gray_img=cor_gray_img * 255
cv2.imwrite(cor_save_path+filename[:-7] + '_'+str(k+1)+'.png', cor_gray_img )
#nii
# cor_gray_img = np.rot90(cor_gray_img, k=3)
# cor_gray_img = np.fliplr(cor_gray_img) # 左右翻转
# img = nib.Nifti1Image(cor_gray_img, np.eye(4))
# nib.save(img,'E:\\bigorgs\\medical_data\\Conary\\Hospital\\dongbei\\2D\\images\\cornal\\' + '1_' + str(k + 1) + ".nii")
# nib.save(img,cor_save_path+filename[:-7] + '_'+str(k+1)+'.nii')
print("---------冠状面转化完成------")
#矢状面
for k in range(ct_value.shape[2]):
#image
# sag_gray_img = ct_value[:, :, k].astype(np.uint8)
sag_gray_img = ct_value_new[:, :, k].astype(np.uint8)
#label
# sag_gray_img = ct_value[:, :, k].astype(np.uint8)
#png
sag_gray_img=np.flipud(sag_gray_img) #上下翻转
# sag_gray_img=sag_gray_img * 255
cv2.imwrite(sag_save_path+filename[:-7] + '_'+str(k+1)+'.png', sag_gray_img )
#nii
# sag_gray_img = np.flipud(sag_gray_img)
# sag_gray_img = np.rot90(sag_gray_img, k=3)
# img = nib.Nifti1Image(sag_gray_img, np.eye(4))
# nib.save(img, 'E:\\bigorgs\\medical_data\\Conary\\Hospital\\dongbei\\2D\\images\\sagittal\\' + '1_' + str(k + 1) + ".nii")
# nib.save(img, sag_save_path+filename[:-7] + '_'+str(k+1)+'.nii')
print("---------矢状面转化完成------")
print("---------全部转化完成------")