-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathMyProcessDicom.py
229 lines (190 loc) · 8.53 KB
/
MyProcessDicom.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
import pydicom
import os,re
import numpy as np
import pandas as pd
import shutil
import matplotlib.pyplot as plt
import SimpleITK as sitk
from IPython.display import clear_output
import math
import sys
import AssistVisualization as vis
from skimage.measure import label
from ipywidgets import interact, fixed
import cv2
def resample_image(fixed_image, space=None):
if space is not None:
isoresample = sitk.ResampleImageFilter()
isoresample.SetInterpolator(sitk.sitkBSpline) # NearestNeighbor
isoresample.SetOutputDirection(fixed_image.GetDirection())
isoresample.SetOutputOrigin(fixed_image.GetOrigin())
orig_size = np.array(fixed_image.GetSize(), dtype=np.int)
new_size = orig_size.copy()
new_size[2] = int(orig_size[2]*(space[2]/space[0])+0.5)
new_size = [int(s) for s in new_size]
print(orig_size, new_size)
isoresample.SetSize(new_size)
orig_spacing = fixed_image.GetSpacing()
new_spacing = (orig_spacing[0],orig_spacing[0],orig_spacing[0]*orig_size[2]/new_size[2])
isoresample.SetOutputSpacing(new_spacing)
return isoresample.Execute(fixed_image)
else:
isoresample = sitk.ResampleImageFilter()
isoresample.SetInterpolator(sitk.sitkBSpline)
isoresample.SetOutputDirection(fixed_image.GetDirection())
isoresample.SetOutputOrigin(fixed_image.GetOrigin())
orig_spacing = fixed_image.GetSpacing()
new_spacing = (orig_spacing[0],orig_spacing[0],orig_spacing[0])
isoresample.SetOutputSpacing(new_spacing)
orig_size = np.array(fixed_image.GetSize(), dtype=np.int)
new_size = orig_size.copy()
new_size[2] = int(orig_size[2]*(orig_spacing[2]/orig_spacing[0])+0.5)
new_size = [int(s) for s in new_size]
print(orig_size, new_size)
isoresample.SetSize(new_size)
return isoresample.Execute(fixed_image)
def resample_bwimage(fixed_image):
isoresample = sitk.ResampleImageFilter()
isoresample.SetInterpolator(sitk.sitkNearestNeighbor)
isoresample.SetOutputDirection(fixed_image.GetDirection())
isoresample.SetOutputOrigin(fixed_image.GetOrigin())
orig_spacing = fixed_image.GetSpacing()
new_spacing = (orig_spacing[0],orig_spacing[0],orig_spacing[0])
isoresample.SetOutputSpacing(new_spacing)
orig_size = np.array(fixed_image.GetSize(), dtype=np.int)
new_size = orig_size.copy()
new_size[2] = int(orig_size[2]*(orig_spacing[2]/orig_spacing[0])+0.5)
new_size = [int(s) for s in new_size]
# print(orig_size, new_size)
isoresample.SetSize(new_size)
return isoresample.Execute(fixed_image)
def adaptive_thresh(images):
new_images = []
for z in range(images.GetDepth()):
img = sitk.GetArrayFromImage(images[:,:,z])
img = img.astype(np.uint8)
binary1 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
# img = cv2.GaussianBlur(img,(5,5),0)
# canny = cv2.Canny(img, 50, 80, apertureSize = 3) # threshold2越大，提取的边缘越少
dst = binary1
size = 2
kernel = np.ones((size, size), dtype=np.uint8)
img_open = cv2.erode(cv2.dilate(dst, kernel), kernel)
new_images.append(img_open)
img_array = np.array(new_images)
return sitk.GetImageFromArray(img_array)
def do_thresh(images):
new_images = []
for z in range(images.GetDepth()):
img = sitk.GetArrayFromImage(images[:,:,z])
img = img.astype(np.uint8)
img = cv2.medianBlur(img,3)
img = cv2.GaussianBlur(img,(3,3),0)
th1, binary1 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
new_images.append(binary1)
img_array = np.array(new_images)
return sitk.GetImageFromArray(img_array)
def empty_preprocess(images):
new_images = []
for z in range(images.GetDepth()):
img = sitk.GetArrayFromImage(images[:,:,z])
img = img.astype(np.uint8)
new_images.append(img)
return sitk.GetImageFromArray(np.array(new_images))
def postprocess(images):
new_images = []
for z in range(images.GetDepth()):
img = sitk.GetArrayFromImage(images[:,:,z])
img = img.astype(np.uint8)
# th1, binary1 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
img = cv2.GaussianBlur(img,(5,5),0)
# th1, binary1 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
dst = img
kernel = np.ones((5, 5), dtype=np.uint8)
img_open = cv2.erode(cv2.dilate(dst, kernel), kernel)
new_images.append(img_open)
img_array = np.array(new_images)
return sitk.GetImageFromArray(img_array)
def get_component_process(images):
new_images = []
for z in range(images.GetDepth()):
img = sitk.GetArrayFromImage(images[:,:,z])
img = img.astype(np.uint8)
kernel = np.ones((2, 2), dtype=np.uint8)
tmp_img = cv2.erode(cv2.dilate(img, kernel), kernel)
# contours, hierarchy = cv2.findContours(img,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
lcc = largestConnectComponent(tmp_img)
# tmp = np.zeros((384,384))
# cv2.drawContours(tmp,contours,-1,(255,255,255),-1)
new_images.append(lcc)
img_array = np.array(new_images)
return sitk.GetImageFromArray(img_array)
def preprocess(images):
tmp_img = do_thresh(images)
tmp_img = adaptive_thresh(tmp_img)
return tmp_img
# def preprocess(images):
# new_images = []
# for z in range(images.GetDepth()):
# img = sitk.GetArrayFromImage(images[:,:,z])
# img = img.astype(np.uint8)
# dst = cv2.equalizeHist(img)
# size = 3
# kernel = np.ones((size, size), dtype=np.uint8)
# img_erosion = cv2.erode(dst, kernel, iterations=1)
# img_dilation = cv2.dilate(dst, kernel, iterations=1)
# img_open = cv2.dilate(cv2.erode(dst, kernel), kernel)
# new_images.append(dst)
# img_array = np.array(new_images)
# return sitk.GetImageFromArray(img_array)
# def edge_detect(images, threshold2=100):
# new_images = []
# for z in range(images.GetDepth()):
# img = sitk.GetArrayFromImage(images[:,:,z])
# img = img.astype(np.uint8)
# img = cv2.GaussianBlur(img,(5,5),0)
# canny = cv2.Canny(img, 50, threshold2, apertureSize = 3) # threshold2越大，提取的边缘越少
# new_images.append(canny)
# img_array = np.array(new_images)
# return sitk.GetImageFromArray(img_array)
def transform_img(fixed_image, moving_image, transform):
resample = sitk.ResampleImageFilter()
resample.SetReferenceImage(fixed_image)
resample.SetInterpolator(sitk.sitkBSpline)
resample.SetTransform(transform)
return resample.Execute(moving_image)
def largestConnectComponent(bw_img):
labeled_img, num = label(bw_img, connectivity=1, background=0, return_num=True)
max_label = 0
max_num = 0
for i in range(1, num+1): # 这里从1开始，防止将背景设置为最大连通域
if np.sum(labeled_img == i) > max_num:
max_num = np.sum(labeled_img == i)
max_label = i
lcc = (labeled_img == max_label)
return lcc.astype(np.uint8)
def show_sagittal_images(fixed_image, moving_image):
# Create a figure with two subplots and the specified size.
plt.subplots(1,2,figsize=(10,8))
fixed_npa = sitk.GetArrayViewFromImage(fixed_image)
moving_npa = sitk.GetArrayViewFromImage(moving_image)
size = np.size(fixed_npa, 2)
# Draw the fixed image in the first subplot.
plt.subplot(1,2,1)
plt.imshow(fixed_npa[:,:,size//2],cmap=plt.cm.Greys_r);
plt.title('fixed image')
plt.axis('off')
# Draw the moving image in the second subplot.
plt.subplot(1,2,2)
plt.imshow(moving_npa[:,:,size//2],cmap=plt.cm.Greys_r);
plt.title('moving image')
plt.axis('off')
plt.show()
def show_masked_images(image, mask):
interact(vis.display_images_with_mask, image_z=(0,image.GetSize()[2] - 1),
fixed = fixed(image), moving=fixed(mask));
def show_images(fixed_image, moving_image):
interact(vis.display_images, fixed_image_z=(0,fixed_image.GetSize()[2]-1), moving_image_z=(0,moving_image.GetSize()[2]-1),
fixed_npa = fixed(sitk.GetArrayViewFromImage(fixed_image)), moving_npa=fixed(sitk.GetArrayViewFromImage(moving_image)));
def show_mixed_images(fixed_image, moving_image):
interact(vis.display_images_with_alpha, image_z=(0,fixed_image.GetSize()[2] - 1), alpha=(0.0,1.0,0.05), fixed = fixed(fixed_image), moving=fixed(moving_image));