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visual_dicoms.py
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visual_dicoms.py
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from matplotlib import image
from numpy.core.fromnumeric import shape
import pylab
import pydicom
import SimpleITK as sitk
import matplotlib.pyplot as plt
import numpy as np
#pydicom read dcm
def dicom_read():
ds=pydicom.read_file("C:\\Users\\57575\\Desktop\\I1000000")
print(ds.dir("pat"))
pix = ds.pixel_array
##读取显示图片
pylab.imshow(pix, cmap=pylab.cm.bone)
pylab.show()
def sitk_read_dcmseries(img_path):
reader = sitk.ImageSeriesReader()
img_names = reader.GetGDCMSeriesFileNames(img_path)
reader.SetFileNames(img_names)
image = reader.Execute()
image_array = sitk.GetArrayFromImage(image)
# print('dicom size:',image.GetSize()) #512*512*208
# spacing = np.array(image.GetSpacing())
# print(spacing)
'''
print meta
'''
# keys = image.GetMetaDataKeys()
# print(keys)
# for key in keys:
# print(key, image.GetMetaData(key))
# print(image_array)
return image_array
def sitk_read_mha(filename):
itk_img = sitk.ReadImage(filename)
img_array = sitk.GetArrayFromImage(itk_img) # indexes are z,y,x (notice the ordering)
# num_z, height, width = img_array.shape #heightXwidth constitute the transverse plane
# origin = np.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
# spacing = np.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
# print('seg size:',img_array.shape)
# print(spacing)
'''
print meta
'''
# keys = itk_img.GetMetaDataKeys()
# for key in keys:
# print (key, itk_img.GetMetaData(key))
# print(img_array[0])
return img_array
def norm_img(image): # 归一化像素值到（0，255）之间，且将溢出值取边界值
MIN_BOUND = -1000.0
MAX_BOUND = 400.0
image = 255* (image - MIN_BOUND) / (MAX_BOUND - MIN_BOUND)
image[image > 255] = 255.
image[image < 0] = 0.
return image
def find_segment_slice(seg_array):
##find segment slice
seg_slice = []
seg_list = []
for i in range(seg_array.shape[0]):
if np.any(seg_array[i,:,]):
seg_slice.append(i)
seg_list.append(np.count_nonzero(seg_array[i,:,]))
# print(np.nonzero(seg_array[90,:,:]))
return seg_slice[np.argmax(seg_list)]
def visualize(ori_array, seg_array, num_z = 0, show_3D = True):
if show_3D == True:
plt.subplot(3,2,1)
plt.imshow(ori_array[:,210,:])
plt.subplot(3,2,2)
plt.imshow(seg_array[:,210,:])
plt.subplot(3,2,3)
plt.imshow(ori_array[:,:,210])
plt.subplot(3,2,4)
plt.imshow(seg_array[:,:,210])
plt.subplot(3,2,5)
plt.imshow(ori_array[num_z,:,:])
plt.subplot(3,2,6)
plt.imshow(seg_array[num_z,:,:])
plt.show()
else:
# plt.subplot(1,2,1)
# plt.imshow(ori_array[num_z,:,:],cmap='gray', vmin = 0, vmax = 200)
# plt.axis('off')
# plt.subplot(1,2,2)
plt.imshow(ori_array[num_z,:,:],cmap='gray', vmin = 0, vmax = 200)
# mask = seg_array[num_z,:,:].T
# for i in range(mask.shape[0]):
# for j in range(mask.shape[1]):
# if mask[i][j] ==1:
# plt.scatter(i,j,c = 'yellow',s =1)
plt.axis('off')
plt.imsave('slice_V.png',ori_array[num_z,:,:], dpi=800 ,cmap='gray', vmin = 0, vmax = 200)
plt.show()
# def visualize_roi(ln3_roi, ln3_array, range):
# for num in range(3):
# plt.subplot(1,2,1)
# plt.imshow(dicom_array[ln3_z1 +num, ln3_y1:ln3_y2, ln3_x1:ln3_x2])
# plt.subplot(1,2,2)
# plt.imshow(dicom_array[ln3_z1 + num, ln3_y1:ln3_y2, ln3_x1:ln3_x2])
# mask = ln3_array[ln3_z1 + num, ln3_y1:ln3_y2, ln3_x1:ln3_x2]
# for i in range(mask.shape[0]):
# for j in range(mask.shape[1]):
# if mask[j][i] ==1:
# plt.scatter(i,j,c = 'yellow',s =1)
# plt.show()
# 对医疗图像进行重采样，仅仅需要将out_spacing替换成自己想要的输出即可
def resample_image(itk_image, out_spacing=[1.0, 1.0, 2.0]):
original_spacing = itk_image.GetSpacing()
original_size = itk_image.GetSize()
# 根据输出out_spacing设置新的size
out_size = [
int(np.round(original_size[0] * original_spacing[0] / out_spacing[0])),
int(np.round(original_size[1] * original_spacing[1] / out_spacing[1])),
int(np.round(original_size[2] * original_spacing[2] / out_spacing[2]))
]
resample = sitk.ResampleImageFilter()
resample.SetOutputSpacing(out_spacing)
resample.SetSize(out_size)
resample.SetOutputDirection(itk_image.GetDirection())
resample.SetOutputOrigin(itk_image.GetOrigin())
resample.SetTransform(sitk.Transform())
resample.SetDefaultPixelValue(itk_image.GetPixelIDValue())
resample.SetInterpolator(sitk.sitkBSpline)
return resample.Execute(itk_image)
# gz_path = 'PANCREAS_0015.nii.gz'
# print('测试文件名为：', gz_path)
# # 使用sitk读取对应的数据
# Original_img = sitk.ReadImage(gz_path)
# print('原始图像的Spacing：', Original_img.GetSpacing())
# print('原始图像的Size：', Original_img.GetSize())
# # 对数据进行重采样
# Resample_img = resample_image(Original_img)
# print('经过resample之后图像的Spacing是：', Resample_img.GetSpacing())
# print('经过resample之后图像的Size是：', Resample_img.GetSize())
if __name__ =='__main__':
# filename = 'E:/radiomic data/High invasive HCC/Center 1-ok/bochanghua/orimeta/1.3.12.2.1107.5.1.4.73756.30000014031400331445300006755.mha'
filename = 'E:/radiomic data/High invasive HCC/Center 2-ok/aojinglai/Orimeta/FILE487.mha'
# filename2 = 'E:/radiomic data/High invasive HCC/Center 1-ok/bochanghua/segmeta/Untitled.mha'
filename2 = r'G:\gastic_ENE\v2\TJtumor_batch1\Data1\P000426300\V\V-P000423765-LN3.mha'
# sitk_read_mha(filename)
dicom_path = r'G:\gastic_ENE\v2\TJtumor_batch1\Data1\P000426300\V'
#208 slice 512*512
dicom_array = sitk_read_dcmseries(dicom_path)
ori_array = sitk_read_mha(filename)
seg_array = sitk_read_mha(filename2)
# print(dicom_array[0])
# print(ori_array[0])
# print(np.max(ori_array))
# print(np.min(ori_array))
ori_array = norm_img(ori_array)
# print(ori_array)
slice = find_segment_slice(seg_array)
# dicom_array = norm_img(dicom_array)
visualize(dicom_array,seg_array,slice,show_3D=False)