-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathfind_normal_cells.py
176 lines (171 loc) · 7.93 KB
/
find_normal_cells.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
import numpy as np
import matplotlib.pyplot as plt
import basefun as bf
import os,shutil
def find_normal_cells(cells_info_file):
cells_info = np.load(cells_info_file)
temp = []
for n in cells_info:
nuclei_area = n['nuclei_area']
temp.append(nuclei_area)
temp1 = np.array(temp)
temp1 = (temp1/20).astype(int)*20
temp2 = list(temp1)
dict_ = {}
for key in temp2:
dict_[key] = dict_.get(key, 0) + 1
# print(dict_)
value = dict_.values()
key = dict_.keys()
value = np.array(list(value))
# print('平滑矩阵',len(value), len(temp3))
temp4 = np.argmax(value)
# print('最大索引',temp4, len(key))
temp5 = list(key)[temp4]
# print('众数',temp5)
limit = temp5*0.3
cnt_ = 0
cells_i = []
for n in cells_info:
nuclei_area = n['nuclei_area']
nuclei_area = np.array(nuclei_area)
if nuclei_area>(temp5-limit) and nuclei_area<(temp5+limit):
cells_i.append(cnt_)
cnt_ = cnt_ + 1
return cells_i
def get_normal_cell_info(cells_info_file ,normal_cells_i):
cells_info = np.load(cells_info_file)
mar_nuclei_area = []
mar_nuclei_rule = []
mar_cell_nuclei_value = []
mar_cytoplasm_area = []
mar_cytoplasm_rule = []
mar_cell_cytoplasm_value = []
mar_cytoplasm_var = []
mar_cell_N_C = []
cnt = 0
for n in cells_info:
if cnt in normal_cells_i:
mar_nuclei_area.append(n['nuclei_area'])
mar_nuclei_rule.append(n['nuclei_rule'])
mar_cell_nuclei_value.append(n['cell_nuclei_value'])
mar_cytoplasm_area.append(n['cytoplasm_area'])
mar_cytoplasm_rule.append(n['cytoplasm_rule'])
mar_cell_cytoplasm_value.append(n['cell_cytoplasm_value'])
mar_cytoplasm_var.append(n['cytoplasm_var'])
mar_cell_N_C.append(n['cell_N_C'])
cnt = cnt + 1
mean_nuclei_area = np.array(mar_nuclei_area).mean()
mean_nuclei_rule = np.array(mar_nuclei_rule).mean()
mean_cell_nuclei_value = np.array(mar_cell_nuclei_value).mean()
mean_cytoplasm_area = np.array(mar_cytoplasm_area).mean()
mean_cytoplasm_rule = np.array(mar_cytoplasm_rule).mean()
mean_cell_cytoplasm_value = np.array(mar_cell_cytoplasm_value).mean()
mean_cytoplasm_var = np.array(mar_cytoplasm_var).mean()
mean_cell_N_C = np.array(mar_cell_N_C).mean()
cell_keys = ['mean_nuclei_area','mean_nuclei_rule','mean_cell_nuclei_value','mean_cytoplasm_area','mean_cytoplasm_rule','mean_cell_cytoplasm_value','mean_cytoplasm_var','mean_cell_N_C']
cell_values = [mean_nuclei_area,mean_nuclei_rule,mean_cell_nuclei_value,mean_cytoplasm_area,mean_cytoplasm_rule,mean_cell_cytoplasm_value,mean_cytoplasm_var,mean_cell_N_C]
cell_dict = dict(zip(cell_keys, cell_values))
return cell_dict
def find_ab_fun(temp,vaule_mean,limit):
cnt = 0
temp_out = []
for n in temp:
if limit > 0:
#print(n-vaule_mean, vaule_mean, limit)
#exit()
if (n-vaule_mean)/vaule_mean > limit:
temp_out.append(1)
else:
temp_out.append(0)
else:
if (n-vaule_mean)/vaule_mean < limit:
temp_out.append(1)
else:
temp_out.append(0)
cnt = cnt + 1
temp_out = np.array(temp_out)
return temp_out
def find_abnormal_cells(cells_info_file, normal_cell_info):
cells_info = np.load(cells_info_file)
mar_nuclei_area = []
mar_nuclei_rule = []
mar_cell_nuclei_value = []
mar_cytoplasm_area = []
mar_cytoplasm_rule = []
mar_cell_cytoplasm_value = []
mar_cytoplasm_var = []
mar_cell_N_C = []
cnt = 0
for n in cells_info:
mar_nuclei_area.append(n['nuclei_area'])
mar_nuclei_rule.append(n['nuclei_rule'])
mar_cell_nuclei_value.append(n['cell_nuclei_value'])
mar_cytoplasm_area.append(n['cytoplasm_area'])
mar_cytoplasm_rule.append(n['cytoplasm_rule'])
mar_cell_cytoplasm_value.append(n['cell_cytoplasm_value'])
mar_cytoplasm_var.append(n['cytoplasm_var'])
mar_cell_N_C.append(n['cell_N_C'])
cnt = cnt + 1
mar_nuclei_area = np.array(mar_nuclei_area)
mar_nuclei_rule = np.array(mar_nuclei_rule)
mar_cell_nuclei_value = np.array(mar_cell_nuclei_value)
mar_cytoplasm_area = np.array(mar_cytoplasm_area)
mar_cytoplasm_rule = np.array(mar_cytoplasm_rule)
mar_cell_cytoplasm_value = np.array(mar_cell_cytoplasm_value)
mar_cytoplasm_var = np.array(mar_cytoplasm_var)
mar_cell_N_C = np.array(mar_cell_N_C)
abnormal_nuclei_area = find_ab_fun(mar_nuclei_area,normal_cell_info['mean_nuclei_area'],2)
abnormal_nuclei_rule = find_ab_fun(mar_nuclei_rule,normal_cell_info['mean_nuclei_rule'],-0.01)
abnormal_cell_nuclei_value = find_ab_fun(mar_cell_nuclei_value,normal_cell_info['mean_cell_nuclei_value'],0.1)
abnormal_cytoplasm_area = find_ab_fun(mar_cytoplasm_area,normal_cell_info['mean_cytoplasm_area'],-0.1)
abnormal_cytoplasm_rule = find_ab_fun(mar_cytoplasm_rule,normal_cell_info['mean_cytoplasm_rule'],-0.02)
abnormal_cell_cytoplasm_value = find_ab_fun(mar_cell_cytoplasm_value,normal_cell_info['mean_cell_cytoplasm_value'],0.1)
abnormal_cytoplasm_var = find_ab_fun(mar_cytoplasm_rule,normal_cell_info['mean_cytoplasm_var'],0.1)
abnormal_cell_N_C = find_ab_fun(mar_cell_N_C,normal_cell_info['mean_cell_N_C'],4)
cells_infos = []
cells_infos = [abnormal_nuclei_area,abnormal_nuclei_rule,abnormal_cell_nuclei_value,abnormal_cytoplasm_area,abnormal_cytoplasm_rule,abnormal_cell_cytoplasm_value,abnormal_cytoplasm_var,abnormal_cell_N_C]
cells_infos = np.array(cells_infos)
cells_infos_2 = cells_infos.copy()
cells_infos_2[0,:] = cells_infos[0,:] * 10 #综合权重
cells_infos_2[1,:] = cells_infos[1,:] * 0
cells_infos_2[2,:] = cells_infos[2,:] * 0
cells_infos_2[3,:] = cells_infos[3,:] * 0
cells_infos_2[4,:] = cells_infos[4,:] * 0
cells_infos_2[5,:] = cells_infos[5,:] * 0
cells_infos_2[5,:] = cells_infos[6,:] * 0
cells_infos_2[6,:] = cells_infos[7,:] * 10
cells_infos_2 = np.array(cells_infos_2)
cnt_2 = 0
for n in range(0, cells_infos_2.shape[1]):
sum_ = sum(cells_infos_2[:,n])
#if sum_ >= 20:
if True:
cellpath = cells_info[n]['cellpath']
nuclei_area_name = cells_info[n]['nuclei_area']
nuclei_rule_name = cells_info[n]['nuclei_rule']
cell_nuclei_value_name = cells_info[n]['cell_nuclei_value']
cytoplasm_area_name = cells_info[n]['cytoplasm_area']
cytoplasm_rule_name = cells_info[n]['cytoplasm_rule']
cell_cytoplasm_value_name = cells_info[n]['cell_cytoplasm_value']
cytoplasm_var_name = cells_info[n]['cytoplasm_var']
cell_N_C_name = cells_info[n]['cell_N_C']
newpath = os.path.join('ab_cells', cellpath.split('/')[1]+'_'+'nuarea'+str(nuclei_area_name)[0:5]+'_'+'nurule'+str(nuclei_rule_name)[0:5]+'_'+'nuvalue'+str(cell_nuclei_value_name)[0:5]+'_'+'cyarea'+str(cytoplasm_area_name)[0:5]+'_'+'cyrule'+str(cytoplasm_rule_name)[0:5]+'_'+'cyvalue'+str(cell_cytoplasm_value_name)[0:5]+'_'+'cyvar'+str(cytoplasm_var_name)[0:5]+'_'+'NC'+str(cell_N_C_name)[0:5]+'.png')
shutil.copy(cellpath, newpath)
cnt_2 = cnt_2 + 1
sign = 0
if cnt_2 > 20: #dd不正常细胞个数大于１０认为为阳性病例
sign = 1
return sign
def main2():
cells_info_file = 'cells_info/cells_info.npy'
normal_cells_i = find_normal_cells(cells_info_file)
normal_cell_info = get_normal_cell_info(cells_info_file, normal_cells_i)
print(normal_cell_info)
sign = find_abnormal_cells(cells_info_file, normal_cell_info)
if __name__ == "__main__":
cells_info_file = 'cells_info/cells_info.npy'
normal_cells_i = find_normal_cells(cells_info_file)
normal_cell_info = get_normal_cell_info(cells_info_file, normal_cells_i)
print(normal_cell_info)
sign = find_abnormal_cells(cells_info_file, normal_cell_info)