/
One_Class_SVM.py
204 lines (149 loc) · 7.76 KB
/
One_Class_SVM.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
"""
Created on Mon Apr 29 05:21:13 2019
"""
import numpy as np
from dipy.viz import window, actor
from dipy.tracking.streamline import transform_streamlines
import vtk.util.colors as colors
from dipy.tracking import utils
from dipy.tracking.streamline import set_number_of_points
from sklearn import svm
import nibabel as nib
from dipy.tracking.vox2track import streamline_mapping
import time
def show_tract(segmented_tract, color_positive ,segmented_tract_negative, color_negative, out_path):
"""Visualization of the segmented tract.
"""
affine=utils.affine_for_trackvis(voxel_size=np.array([1.25,1.25,1.25]))
bundle_native = transform_streamlines(segmented_tract, np.linalg.inv(affine))
bundle_nativeNeg = transform_streamlines(segmented_tract_negative, np.linalg.inv(affine))
renderer = window.Renderer()
stream_actor2 = actor.line(bundle_native,
colors=color_positive, linewidth=0.1)
stream_actorNeg = actor.line(bundle_nativeNeg, colors=color_negative,
opacity=0.01, linewidth=0.1)
renderer.set_camera(position=(408.85, -26.23, 92.12),
focal_point=(0.42, -14.03, 0.82),
view_up=(-0.09, 0.85, 0.51))
bar = actor.scalar_bar()
renderer.add(stream_actor2)
renderer.add(stream_actorNeg)
renderer.add(bar)
window.show(renderer, size=(1920, 1039), reset_camera=False)
renderer.camera_info()
"""Take a snapshot of the window and save it
"""
window.record(renderer, out_path = out_path, size=(1920, 1039))
def compute_dsc(estimated_tract, true_tract):
"""Compute the overlap between the segmented tract and ground truth tract
"""
aff=np.array([[-1.25, 0, 0, 90],[0, 1.25, 0, -126],[0, 0, 1.25, -72],[0, 0, 0, 1]])
#aff=utils.affine_for_trackvis(voxel_size=np.array([1.25,1.25,1.25]))
voxel_list_estimated_tract = streamline_mapping(estimated_tract, affine=aff).keys()
voxel_list_true_tract = streamline_mapping(true_tract, affine=aff).keys()
TP = len(set(voxel_list_estimated_tract).intersection(set(voxel_list_true_tract)))
vol_A = len(set(voxel_list_estimated_tract))
vol_B = len(set(voxel_list_true_tract))
DSC = 2.0 * float(TP) / float(vol_A + vol_B)
return DSC
def load(filename):
"""Load tractogram from TRK file
"""
wholeTract= nib.streamlines.load(filename)
wholeTract = wholeTract.streamlines
return wholeTract
def embedding(streamlines, no_of_points):
"""Resample streamlines using 12 points and also flatten the streamlines
"""
return np.array([set_number_of_points(s, no_of_points).ravel() for s in streamlines])
def create_train_data_set(train_subjectList,tract):
train_data=[]
for sub in train_subjectList:
print (sub)
T_filename=sub+tract
wholeTract = load (T_filename)
train_data=np.concatenate((train_data, wholeTract),axis=0)
print ("train data Shape")
resample_tract=embedding(train_data,no_of_points=no_of_points)
return resample_tract, train_data
def create_test_data_set(testTarget_brain):
print ("Preparing Test Data")
t_filename=testTarget_brain #"124422_af.left.trk"
test_data=load(t_filename)
resample_tractogram=embedding(test_data,no_of_points=no_of_points)
return resample_tractogram, test_data
if __name__ == '__main__':
train_subjectList =[ "124422", "111312", "100408", "100307", "856766"]
tract = "_cg.right.trk"
no_of_points=12
leafsize=10
################################ Train Data ######################################
print ("Preparing Train Data")
resample_tract_train, train_data= create_train_data_set(train_subjectList, tract)
###################### Test Data################################
testTarget = "161731"
testTarget_brain = "full1M_"+testTarget+".trk"
t0=time.time()
resample_tract_test, test_data= create_test_data_set(testTarget_brain)
trueTract=load(testTarget + tract)
t1=t0-time.time()
"""########################### one class SVM Linear ######################"""
gamma_value = 0.001
clf = svm.OneClassSVM(nu=0.1, kernel="linear", gamma=gamma_value)
clf.fit(resample_tract_train)
""" linear poly rbf """
"""#########################################"""
x_pred_train = clf.predict(resample_tract_train.tolist())
n_error_test = x_pred_train[x_pred_train==-1].size
print('number of error for training =', n_error_test)
x_pred_test=clf.predict(resample_tract_test.tolist())
n_error_test = x_pred_test[x_pred_test==-1].size
print('number of error for testing=',n_error_test)
########################### visualize tract ######################
test_data=np.array(test_data)
segmented_tract_positive= test_data[np.where(x_pred_test==1)]
segmented_tract_negative= test_data[np.where(x_pred_test==-1)]
dsc=compute_dsc(segmented_tract_positive,trueTract)
print("Accuracy for linear: ",dsc)
"""########################### one class SVM Poly ######################"""
gamma_value = 0.001
clf = svm.OneClassSVM(nu=0.1, kernel="poly", gamma=gamma_value)
clf.fit(resample_tract_train)
""" linear poly rbf """
"""#########################################"""
x_pred_train = clf.predict(resample_tract_train.tolist())
n_error_test = x_pred_train[x_pred_train==-1].size
print('number of error for training =', n_error_test)
x_pred_test=clf.predict(resample_tract_test.tolist())
n_error_test = x_pred_test[x_pred_test==-1].size
print('number of error for testing=',n_error_test)
########################### visualize tract ######################
test_data=np.array(test_data)
segmented_tract_positive= test_data[np.where(x_pred_test==1)]
segmented_tract_negative= test_data[np.where(x_pred_test==-1)]
dsc=compute_dsc(segmented_tract_positive,trueTract)
print("Accuracy for poly: ",dsc)
"""########################### one class SVM RBF######################"""
t2=time.time()
gamma_value = 0.001
clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=gamma_value)
clf.fit(resample_tract_train)
x_pred_train = clf.predict(resample_tract_train.tolist())
n_error_test = x_pred_train[x_pred_train==-1].size
print('number of error for training =', n_error_test)
x_pred_test=clf.predict(resample_tract_test.tolist())
n_error_test = x_pred_test[x_pred_test==-1].size
print('number of error for testing=',n_error_test)
########################### visualize tract ######################
test_data=np.array(test_data)
segmented_tract_positive= test_data[np.where(x_pred_test==1)]
segmented_tract_negative= test_data[np.where(x_pred_test==-1)]
###########################Calculating Dice Similarity Co-efficient###########################
dsc=compute_dsc(segmented_tract_positive,trueTract)
print("Accuracy for rbf: ",dsc)
print("Total amount of time to compute svm is %f seconds" % ((time.time()-t2)+t1))
print("Show the tract")
out_path="images\\svm\\"+str(len(train_subjectList) )+"_sub_"+testTarget+"_"+tract+"_SVMResult.png" # Save image in this path
color_positive= colors.green
color_negative=colors.red
show_tract(segmented_tract_positive, color_positive, segmented_tract_negative, color_negative, out_path)#,color_negative)#,segmented_tract_negative)