-
Notifications
You must be signed in to change notification settings - Fork 521
/
image.py
247 lines (189 loc) · 7.96 KB
/
image.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
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
from ..utils.filemanip import fname_presuffix
from .base import SimpleInterface, TraitedSpec, BaseInterfaceInputSpec, traits, File
from .. import LooseVersion
class RescaleInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="Skull-stripped image to rescale")
ref_file = File(exists=True, mandatory=True, desc="Skull-stripped reference image")
invert = traits.Bool(desc="Invert contrast of rescaled image")
percentile = traits.Range(
low=0.0,
high=50.0,
value=0.0,
usedefault=True,
desc="Percentile to use for reference to allow "
"for outliers - 1 indicates the 1st and "
"99th percentiles in the input file will "
"be mapped to the 99th and 1st percentiles "
"in the reference; 0 indicates minima and "
"maxima will be mapped",
)
class RescaleOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="Rescaled image")
class Rescale(SimpleInterface):
"""Rescale an image
Rescales the non-zero portion of ``in_file`` to match the bounds of the
non-zero portion of ``ref_file``.
Reference values in the input and reference images are defined by the
``percentile`` parameter, and the reference values in each image are
identified and the remaining values are scaled accordingly.
In the case of ``percentile == 0``, the reference values are the maxima
and minima of each image.
If the ``invert`` parameter is set, the input file is inverted prior to
rescaling.
Examples
--------
To use a high-resolution T1w image as a registration target for a T2\\*
image, it may be useful to invert the T1w image and rescale to the T2\\*
range.
Using the 1st and 99th percentiles may reduce the impact of outlier
voxels.
>>> from nipype.interfaces.image import Rescale
>>> invert_t1w = Rescale(invert=True)
>>> invert_t1w.inputs.in_file = 'structural.nii'
>>> invert_t1w.inputs.ref_file = 'functional.nii'
>>> invert_t1w.inputs.percentile = 1.
>>> res = invert_t1w.run() # doctest: +SKIP
"""
input_spec = RescaleInputSpec
output_spec = RescaleOutputSpec
def _run_interface(self, runtime):
import numpy as np
import nibabel as nb
img = nb.load(self.inputs.in_file)
data = img.get_fdata()
ref_data = nb.load(self.inputs.ref_file).get_fdata()
in_mask = data > 0
ref_mask = ref_data > 0
q = [self.inputs.percentile, 100.0 - self.inputs.percentile]
in_low, in_high = np.percentile(data[in_mask], q)
ref_low, ref_high = np.percentile(ref_data[ref_mask], q)
scale_factor = (ref_high - ref_low) / (in_high - in_low)
signal = in_high - data if self.inputs.invert else data - in_low
out_data = in_mask * (signal * scale_factor + ref_low)
suffix = "_inv" if self.inputs.invert else "_rescaled"
out_file = fname_presuffix(
self.inputs.in_file, suffix=suffix, newpath=runtime.cwd
)
img.__class__(out_data, img.affine, img.header).to_filename(out_file)
self._results["out_file"] = out_file
return runtime
_axes = ("RL", "AP", "SI")
_orientations = tuple(
"".join((x[i], y[j], z[k]))
for x in _axes
for y in _axes
for z in _axes
if x != y != z != x
for i in (0, 1)
for j in (0, 1)
for k in (0, 1)
)
class ReorientInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="Input image")
orientation = traits.Enum(
_orientations, usedefault=True, desc="Target axis orientation"
)
class ReorientOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="Reoriented image")
transform = File(
exists=True, desc="Affine transform from input orientation to output"
)
class Reorient(SimpleInterface):
"""Conform an image to a given orientation
Flips and reorder the image data array so that the axes match the
directions indicated in ``orientation``.
The default ``RAS`` orientation corresponds to the first axis being ordered
from left to right, the second axis from posterior to anterior, and the
third axis from inferior to superior.
For oblique images, the original orientation is considered to be the
closest plumb orientation.
No resampling is performed, and thus the output image is not de-obliqued
or registered to any other image or template.
The effective transform is calculated from the original affine matrix to
the reoriented affine matrix.
Examples
--------
If an image is not reoriented, the original file is not modified
.. testsetup::
>>> def print_affine(matrix):
... print(str(matrix).replace(']', ' ').replace('[', ' '))
>>> import numpy as np
>>> from nipype.interfaces.image import Reorient
>>> reorient = Reorient(orientation='LPS')
>>> reorient.inputs.in_file = 'segmentation0.nii.gz'
>>> res = reorient.run()
>>> res.outputs.out_file
'segmentation0.nii.gz'
>>> print_affine(np.loadtxt(res.outputs.transform))
1. 0. 0. 0.
0. 1. 0. 0.
0. 0. 1. 0.
0. 0. 0. 1.
>>> reorient.inputs.orientation = 'RAS'
>>> res = reorient.run()
>>> res.outputs.out_file # doctest: +ELLIPSIS
'.../segmentation0_ras.nii.gz'
>>> print_affine(np.loadtxt(res.outputs.transform))
-1. 0. 0. 60.
0. -1. 0. 72.
0. 0. 1. 0.
0. 0. 0. 1.
.. testcleanup::
>>> import os
>>> os.unlink(res.outputs.out_file)
>>> os.unlink(res.outputs.transform)
"""
input_spec = ReorientInputSpec
output_spec = ReorientOutputSpec
def _run_interface(self, runtime):
import numpy as np
import nibabel as nb
from nibabel.orientations import axcodes2ornt, ornt_transform, inv_ornt_aff
fname = self.inputs.in_file
orig_img = nb.load(fname)
# Find transform from current (approximate) orientation to
# target, in nibabel orientation matrix and affine forms
orig_ornt = nb.io_orientation(orig_img.affine)
targ_ornt = axcodes2ornt(self.inputs.orientation)
transform = ornt_transform(orig_ornt, targ_ornt)
affine_xfm = inv_ornt_aff(transform, orig_img.shape)
# Check can be eliminated when minimum nibabel version >= 2.4
if LooseVersion(nb.__version__) >= LooseVersion("2.4.0"):
reoriented = orig_img.as_reoriented(transform)
else:
reoriented = _as_reoriented_backport(orig_img, transform)
# Image may be reoriented
if reoriented is not orig_img:
suffix = "_" + self.inputs.orientation.lower()
out_name = fname_presuffix(fname, suffix=suffix, newpath=runtime.cwd)
reoriented.to_filename(out_name)
else:
out_name = fname
mat_name = fname_presuffix(
fname, suffix=".mat", newpath=runtime.cwd, use_ext=False
)
np.savetxt(mat_name, affine_xfm, fmt="%.08f")
self._results["out_file"] = out_name
self._results["transform"] = mat_name
return runtime
def _as_reoriented_backport(img, ornt):
"""Backport of img.as_reoriented as of nibabel 2.4.0"""
import numpy as np
import nibabel as nb
from nibabel.orientations import inv_ornt_aff
if np.array_equal(ornt, [[0, 1], [1, 1], [2, 1]]):
return img
t_arr = nb.apply_orientation(img.dataobj, ornt)
new_aff = img.affine.dot(inv_ornt_aff(ornt, img.shape))
reoriented = img.__class__(t_arr, new_aff, img.header)
if isinstance(reoriented, nb.Nifti1Pair):
# Also apply the transform to the dim_info fields
new_dim = [
None if orig_dim is None else int(ornt[orig_dim, 0])
for orig_dim in img.header.get_dim_info()
]
reoriented.header.set_dim_info(*new_dim)
return reoriented