def check_if_rpi(fname):
from sct_orientation import get_orientation
if not get_orientation(fname) == 'RPI':
printv(
'\nERROR: ' + fname +
' is not in RPI orientation. Use sct_orientation to reorient your data. Exit program.\n',
1, 'error')
def change_orientation(self, orientation='RPI', inversion_orient=False):
"""
This function changes the orientation of the data by swapping the image axis.
Warning: the nifti image header is not changed!!!
:param orientation: string of three character representing the new orientation (ex: AIL, default: RPI)
inversion_orient: boolean. If True, the data change to match the orientation in the header, based on the orientation provided as the argument orientation.
:return:
"""
opposite_character = {'L': 'R', 'R': 'L', 'A': 'P', 'P': 'A', 'I': 'S', 'S': 'I'}
if self.orientation is None:
from sct_orientation import get_orientation
self.orientation = get_orientation(self.file_name)
# get orientation to return at the end of function
raw_orientation = self.orientation
if inversion_orient:
temp_orientation = self.orientation
self.orientation = orientation
orientation = temp_orientation
# change the orientation of the image
perm = [0, 1, 2]
inversion = [1, 1, 1]
for i, character in enumerate(self.orientation):
try:
perm[i] = orientation.index(character)
except ValueError:
perm[i] = orientation.index(opposite_character[character])
inversion[i] = -1
# axes inversion
self.data = self.data[::inversion[0], ::inversion[1], ::inversion[2]]
# axes manipulations
from numpy import swapaxes
if perm == [1, 0, 2]:
self.data = swapaxes(self.data, 0, 1)
elif perm == [2, 1, 0]:
self.data = swapaxes(self.data, 0, 2)
elif perm == [0, 2, 1]:
self.data = swapaxes(self.data, 1, 2)
elif perm == [2, 1, 0]:
self.data = swapaxes(self.data, 0, 2)
elif perm == [2, 0, 1]:
self.data = swapaxes(self.data, 0, 2) # transform [2, 0, 1] to [1, 0, 2]
self.data = swapaxes(self.data, 0, 1) # transform [1, 0, 2] to [0, 1, 2]
elif perm == [1, 2, 0]:
self.data = swapaxes(self.data, 0, 2) # transform [1, 2, 0] to [0, 2, 1]
self.data = swapaxes(self.data, 1, 2) # transform [0, 2, 1] to [0, 1, 2]
elif perm == [0, 1, 2]:
# do nothing
pass
else:
print 'Error: wrong orientation'
# from numpy import array
# self.dim = array(self.dim)[perm]
self.orientation = orientation
return raw_orientation
def change_orientation(self, orientation="RPI", inversion_orient=False):
"""
This function changes the orientation of the data by swapping the image axis.
Warning: the nifti image header is not changed!!!
:param orientation: string of three character representing the new orientation (ex: AIL, default: RPI)
inversion_orient: boolean. If True, the data change to match the orientation in the header, based on the orientation provided as the argument orientation.
:return:
"""
opposite_character = {"L": "R", "R": "L", "A": "P", "P": "A", "I": "S", "S": "I"}
if self.orientation is None:
from sct_orientation import get_orientation
self.orientation = get_orientation(self.file_name)
if inversion_orient:
temp_orientation = self.orientation
self.orientation = orientation
orientation = temp_orientation
# change the orientation of the image
perm = [0, 1, 2]
inversion = [1, 1, 1]
for i, character in enumerate(self.orientation):
try:
perm[i] = orientation.index(character)
except ValueError:
perm[i] = orientation.index(opposite_character[character])
inversion[i] = -1
# axes inversion
self.data = self.data[:: inversion[0], :: inversion[1], :: inversion[2]]
# axes manipulations
from numpy import swapaxes
if perm == [1, 0, 2]:
self.data = swapaxes(self.data, 0, 1)
elif perm == [2, 1, 0]:
self.data = swapaxes(self.data, 0, 2)
elif perm == [0, 2, 1]:
self.data = swapaxes(self.data, 1, 2)
elif perm == [2, 1, 0]:
self.data = swapaxes(self.data, 0, 2)
elif perm == [2, 0, 1]:
self.data = swapaxes(self.data, 0, 2) # transform [2, 0, 1] to [1, 0, 2]
self.data = swapaxes(self.data, 0, 1) # transform [1, 0, 2] to [0, 1, 2]
elif perm == [1, 2, 0]:
self.data = swapaxes(self.data, 0, 2) # transform [1, 2, 0] to [0, 2, 1]
self.data = swapaxes(self.data, 1, 2) # transform [0, 2, 1] to [0, 1, 2]
elif perm == [0, 1, 2]:
# do nothing
pass
else:
print "Error: wrong orientation"
self.orientation = orientation
def loadFromPath(self, path, verbose):
"""
This function load an image from an absolute path using nibabel library
:param path: path of the file from which the image will be loaded
:return:
"""
from nibabel import load, spatialimages
from sct_utils import check_file_exist, printv, extract_fname
from sct_orientation import get_orientation
check_file_exist(path, verbose=verbose)
try:
im_file = load(path)
except spatialimages.ImageFileError:
printv('Error: make sure ' + path + ' is an image.', 1, 'error')
self.orientation = get_orientation(path)
self.data = im_file.get_data()
self.hdr = im_file.get_header()
self.absolutepath = path
self.path, self.file_name, self.ext = extract_fname(path)
def loadFromPath(self, path, verbose):
"""
This function load an image from an absolute path using nibabel library
:param path: path of the file from which the image will be loaded
:return:
"""
from nibabel import load, spatialimages
from sct_utils import check_file_exist, printv, extract_fname
from sct_orientation import get_orientation
# check_file_exist(path, verbose=verbose)
try:
im_file = load(path)
except spatialimages.ImageFileError:
printv('Error: make sure ' + path + ' is an image.', 1, 'error')
self.orientation = get_orientation(path)
self.data = im_file.get_data()
self.hdr = im_file.get_header()
self.absolutepath = path
self.path, self.file_name, self.ext = extract_fname(path)
self.dim = get_dimension(im_file)
def compute_csa(fname_segmentation,
name_method,
volume_output,
verbose,
remove_temp_files,
step,
smoothing_param,
figure_fit,
name_output,
slices,
vert_levels,
path_to_template,
algo_fitting='hanning',
type_window='hanning',
window_length=80):
# Extract path, file and extension
fname_segmentation = os.path.abspath(fname_segmentation)
path_data, file_data, ext_data = sct.extract_fname(fname_segmentation)
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = sct.slash_at_the_end('tmp.' + time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir ' + path_tmp, verbose)
# Copying input data to tmp folder and convert to nii
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
sct.run('isct_c3d ' + fname_segmentation + ' -o ' + path_tmp +
'segmentation.nii')
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input segmentation into RPI
sct.printv('\nChange orientation of the input segmentation into RPI...',
verbose)
fname_segmentation_orient = set_orientation('segmentation.nii', 'RPI',
'segmentation_orient.nii')
# Get size of data
sct.printv('\nGet data dimensions...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_segmentation_orient).dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# Open segmentation volume
sct.printv('\nOpen segmentation volume...', verbose)
file_seg = nibabel.load(fname_segmentation_orient)
data_seg = file_seg.get_data()
hdr_seg = file_seg.get_header()
# # Extract min and max index in Z direction
X, Y, Z = (data_seg > 0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
# Xp, Yp = (data_seg[:, :, 0] >= 0).nonzero() # X and Y range
# extract centerline and smooth it
x_centerline_fit, y_centerline_fit, z_centerline, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = smooth_centerline(
fname_segmentation_orient,
algo_fitting=algo_fitting,
type_window=type_window,
window_length=window_length,
verbose=verbose)
z_centerline_scaled = [x * pz for x in z_centerline]
# Compute CSA
sct.printv('\nCompute CSA...', verbose)
# Empty arrays in which CSA for each z slice will be stored
csa = np.zeros(max_z_index - min_z_index + 1)
# csa = [0.0 for i in xrange(0, max_z_index-min_z_index+1)]
for iz in xrange(0, len(z_centerline)):
# compute the vector normal to the plane
normal = normalize(
np.array([
x_centerline_deriv[iz], y_centerline_deriv[iz],
z_centerline_deriv[iz]
]))
# compute the angle between the normal vector of the plane and the vector z
angle = np.arccos(np.dot(normal, [0, 0, 1]))
# compute the number of voxels, assuming the segmentation is coded for partial volume effect between 0 and 1.
number_voxels = sum(sum(data_seg[:, :, iz + min_z_index]))
# compute CSA, by scaling with voxel size (in mm) and adjusting for oblique plane
csa[iz] = number_voxels * px * py * np.cos(angle)
if smoothing_param:
from msct_smooth import smoothing_window
sct.printv('\nSmooth CSA across slices...', verbose)
sct.printv('.. Hanning window: ' + str(smoothing_param) + ' mm',
verbose)
csa_smooth = smoothing_window(csa,
window_len=smoothing_param / pz,
window='hanning',
verbose=0)
# display figure
if verbose == 2:
import matplotlib.pyplot as plt
plt.figure()
pltx, = plt.plot(z_centerline_scaled, csa, 'bo')
pltx_fit, = plt.plot(z_centerline_scaled,
csa_smooth,
'r',
linewidth=2)
plt.title("Cross-sectional area (CSA)")
plt.xlabel('z (mm)')
plt.ylabel('CSA (mm^2)')
plt.legend([pltx, pltx_fit], ['Raw', 'Smoothed'])
plt.show()
# update variable
csa = csa_smooth
# Create output text file
sct.printv('\nWrite text file...', verbose)
file_results = open('csa.txt', 'w')
for i in range(min_z_index, max_z_index + 1):
file_results.write(
str(int(i)) + ',' + str(csa[i - min_z_index]) + '\n')
# Display results
sct.printv(
'z=' + str(i - min_z_index) + ': ' + str(csa[i - min_z_index]) +
' mm^2', verbose, 'bold')
file_results.close()
# output volume of csa values
if volume_output:
sct.printv('\nCreate volume of CSA values...', verbose)
# get orientation of the input data
orientation = get_orientation('segmentation.nii')
data_seg = data_seg.astype(np.float32, copy=False)
# loop across slices
for iz in range(min_z_index, max_z_index + 1):
# retrieve seg pixels
x_seg, y_seg = (data_seg[:, :, iz] > 0).nonzero()
seg = [[x_seg[i], y_seg[i]] for i in range(0, len(x_seg))]
# loop across pixels in segmentation
for i in seg:
# replace value with csa value
data_seg[i[0], i[1], iz] = csa[iz - min_z_index]
# create header
hdr_seg.set_data_dtype('float32') # set imagetype to uint8
# save volume
img = nibabel.Nifti1Image(data_seg, None, hdr_seg)
nibabel.save(img, 'csa_RPI.nii')
# Change orientation of the output centerline into input orientation
fname_csa_volume = set_orientation('csa_RPI.nii', orientation,
'csa_RPI_orient.nii')
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
from shutil import copyfile
copyfile(path_tmp + 'csa.txt', path_data + param.fname_csa)
# sct.generate_output_file(path_tmp+'csa.txt', path_data+param.fname_csa) # extension already included in param.fname_csa
if volume_output:
sct.generate_output_file(
fname_csa_volume, path_data +
name_output) # extension already included in name_output
# average csa across vertebral levels or slices if asked (flag -z or -l)
if slices or vert_levels:
if vert_levels and not path_to_template:
sct.printv('\nERROR: Path to template is missing. See usage.\n', 1,
'error')
sys.exit(2)
elif vert_levels and path_to_template:
abs_path_to_template = os.path.abspath(path_to_template)
# go to tmp folder
os.chdir(path_tmp)
# create temporary folder
sct.printv('\nCreate temporary folder to average csa...', verbose)
path_tmp_extract_metric = sct.slash_at_the_end('label_temp', 1)
sct.run('mkdir ' + path_tmp_extract_metric, verbose)
# Copying output CSA volume in the temporary folder
sct.printv('\nCopy data to tmp folder...', verbose)
sct.run('cp ' + fname_segmentation + ' ' + path_tmp_extract_metric)
# create file info_label
path_fname_seg, file_fname_seg, ext_fname_seg = sct.extract_fname(
fname_segmentation)
create_info_label('info_label.txt', path_tmp_extract_metric,
file_fname_seg + ext_fname_seg)
# average CSA
if slices:
os.system("sct_extract_metric -i " + path_data + name_output +
" -f " + path_tmp_extract_metric +
" -m wa -o ../csa_mean.txt -z " + slices)
if vert_levels:
sct.run('cp -R ' + abs_path_to_template + ' .')
os.system("sct_extract_metric -i " + path_data + name_output +
" -f " + path_tmp_extract_metric +
" -m wa -o ../csa_mean.txt -v " + vert_levels)
os.chdir('..')
# Remove temporary files
print('\nRemove temporary folder used to average CSA...')
sct.run('rm -rf ' + path_tmp_extract_metric)
# Remove temporary files
if remove_temp_files:
print('\nRemove temporary files...')
sct.run('rm -rf ' + path_tmp)
def extract_centerline(fname_segmentation,
remove_temp_files,
name_output='',
verbose=0,
algo_fitting='hanning',
type_window='hanning',
window_length=80):
# Extract path, file and extension
fname_segmentation = os.path.abspath(fname_segmentation)
path_data, file_data, ext_data = sct.extract_fname(fname_segmentation)
# create temporary folder
path_tmp = 'tmp.' + time.strftime("%y%m%d%H%M%S")
sct.run('mkdir ' + path_tmp)
# copy files into tmp folder
sct.run('cp ' + fname_segmentation + ' ' + path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input centerline into RPI
sct.printv('\nOrient centerline to RPI orientation...', verbose)
fname_segmentation_orient = 'segmentation_rpi' + ext_data
set_orientation(file_data + ext_data, 'RPI', fname_segmentation_orient)
# Get dimension
sct.printv('\nGet dimensions...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_segmentation_orient).dim
sct.printv(
'.. matrix size: ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz),
verbose)
sct.printv(
'.. voxel size: ' + str(px) + 'mm x ' + str(py) + 'mm x ' + str(pz) +
'mm', verbose)
# Extract orientation of the input segmentation
orientation = get_orientation(file_data + ext_data)
sct.printv('\nOrientation of segmentation image: ' + orientation, verbose)
sct.printv('\nOpen segmentation volume...', verbose)
file = nibabel.load(fname_segmentation_orient)
data = file.get_data()
hdr = file.get_header()
# Extract min and max index in Z direction
X, Y, Z = (data > 0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
x_centerline = [0 for i in range(0, max_z_index - min_z_index + 1)]
y_centerline = [0 for i in range(0, max_z_index - min_z_index + 1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index + 1)]
# Extract segmentation points and average per slice
for iz in range(min_z_index, max_z_index + 1):
x_seg, y_seg = (data[:, :, iz] > 0).nonzero()
x_centerline[iz - min_z_index] = np.mean(x_seg)
y_centerline[iz - min_z_index] = np.mean(y_seg)
for k in range(len(X)):
data[X[k], Y[k], Z[k]] = 0
# extract centerline and smooth it
x_centerline_fit, y_centerline_fit, z_centerline_fit, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = smooth_centerline(
fname_segmentation_orient,
type_window=type_window,
window_length=window_length,
algo_fitting=algo_fitting,
verbose=verbose)
if verbose == 2:
import matplotlib.pyplot as plt
#Creation of a vector x that takes into account the distance between the labels
nz_nonz = len(z_centerline)
x_display = [0 for i in range(x_centerline_fit.shape[0])]
y_display = [0 for i in range(y_centerline_fit.shape[0])]
for i in range(0, nz_nonz, 1):
x_display[int(z_centerline[i] - z_centerline[0])] = x_centerline[i]
y_display[int(z_centerline[i] - z_centerline[0])] = y_centerline[i]
plt.figure(1)
plt.subplot(2, 1, 1)
plt.plot(z_centerline_fit, x_display, 'ro')
plt.plot(z_centerline_fit, x_centerline_fit)
plt.xlabel("Z")
plt.ylabel("X")
plt.title("x and x_fit coordinates")
plt.subplot(2, 1, 2)
plt.plot(z_centerline_fit, y_display, 'ro')
plt.plot(z_centerline_fit, y_centerline_fit)
plt.xlabel("Z")
plt.ylabel("Y")
plt.title("y and y_fit coordinates")
plt.show()
# Create an image with the centerline
for iz in range(min_z_index, max_z_index + 1):
data[
round(x_centerline_fit[iz - min_z_index]),
round(y_centerline_fit[iz - min_z_index]),
iz] = 1 # if index is out of bounds here for hanning: either the segmentation has holes or labels have been added to the file
# Write the centerline image in RPI orientation
hdr.set_data_dtype('uint8') # set imagetype to uint8
sct.printv('\nWrite NIFTI volumes...', verbose)
img = nibabel.Nifti1Image(data, None, hdr)
nibabel.save(img, 'centerline.nii.gz')
# Define name if output name is not specified
if name_output == 'csa_volume.nii.gz' or name_output == '':
# sct.generate_output_file('centerline.nii.gz', file_data+'_centerline'+ext_data, verbose)
name_output = file_data + '_centerline' + ext_data
sct.generate_output_file('centerline.nii.gz', name_output, verbose)
# create a txt file with the centerline
path, rad_output, ext = sct.extract_fname(name_output)
name_output_txt = rad_output + '.txt'
sct.printv('\nWrite text file...', verbose)
file_results = open(name_output_txt, 'w')
for i in range(min_z_index, max_z_index + 1):
file_results.write(
str(int(i)) + ' ' + str(x_centerline_fit[i - min_z_index]) + ' ' +
str(y_centerline_fit[i - min_z_index]) + '\n')
file_results.close()
# Copy result into parent folder
sct.run('cp ' + name_output_txt + ' ../')
del data
# come back to parent folder
os.chdir('..')
# Change orientation of the output centerline into input orientation
sct.printv(
'\nOrient centerline image to input orientation: ' + orientation,
verbose)
fname_segmentation_orient = 'tmp.segmentation_rpi' + ext_data
set_orientation(path_tmp + '/' + name_output, orientation, name_output)
# Remove temporary files
if remove_temp_files:
sct.printv('\nRemove temporary files...', verbose)
sct.run('rm -rf ' + path_tmp, verbose)
return name_output
def extract_centerline(
fname_segmentation, remove_temp_files, verbose=0, algo_fitting="hanning", type_window="hanning", window_length=80
):
# Extract path, file and extension
fname_segmentation = os.path.abspath(fname_segmentation)
path_data, file_data, ext_data = sct.extract_fname(fname_segmentation)
# create temporary folder
path_tmp = "tmp." + time.strftime("%y%m%d%H%M%S")
sct.run("mkdir " + path_tmp)
# copy files into tmp folder
sct.run("cp " + fname_segmentation + " " + path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input centerline into RPI
sct.printv("\nOrient centerline to RPI orientation...", verbose)
fname_segmentation_orient = "segmentation_rpi" + ext_data
set_orientation(file_data + ext_data, "RPI", fname_segmentation_orient)
# Get dimension
sct.printv("\nGet dimensions...", verbose)
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_segmentation_orient)
sct.printv(".. matrix size: " + str(nx) + " x " + str(ny) + " x " + str(nz), verbose)
sct.printv(".. voxel size: " + str(px) + "mm x " + str(py) + "mm x " + str(pz) + "mm", verbose)
# Extract orientation of the input segmentation
orientation = get_orientation(file_data + ext_data)
sct.printv("\nOrientation of segmentation image: " + orientation, verbose)
sct.printv("\nOpen segmentation volume...", verbose)
file = nibabel.load(fname_segmentation_orient)
data = file.get_data()
hdr = file.get_header()
# Extract min and max index in Z direction
X, Y, Z = (data > 0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
x_centerline = [0 for i in range(0, max_z_index - min_z_index + 1)]
y_centerline = [0 for i in range(0, max_z_index - min_z_index + 1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index + 1)]
# Extract segmentation points and average per slice
for iz in range(min_z_index, max_z_index + 1):
x_seg, y_seg = (data[:, :, iz] > 0).nonzero()
x_centerline[iz - min_z_index] = np.mean(x_seg)
y_centerline[iz - min_z_index] = np.mean(y_seg)
for k in range(len(X)):
data[X[k], Y[k], Z[k]] = 0
# extract centerline and smooth it
x_centerline_fit, y_centerline_fit, z_centerline_fit, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = smooth_centerline(
fname_segmentation_orient,
type_window=type_window,
window_length=window_length,
algo_fitting=algo_fitting,
verbose=verbose,
)
if verbose == 2:
import matplotlib.pyplot as plt
# Creation of a vector x that takes into account the distance between the labels
nz_nonz = len(z_centerline)
x_display = [0 for i in range(x_centerline_fit.shape[0])]
y_display = [0 for i in range(y_centerline_fit.shape[0])]
for i in range(0, nz_nonz, 1):
x_display[int(z_centerline[i] - z_centerline[0])] = x_centerline[i]
y_display[int(z_centerline[i] - z_centerline[0])] = y_centerline[i]
plt.figure(1)
plt.subplot(2, 1, 1)
plt.plot(z_centerline_fit, x_display, "ro")
plt.plot(z_centerline_fit, x_centerline_fit)
plt.xlabel("Z")
plt.ylabel("X")
plt.title("x and x_fit coordinates")
plt.subplot(2, 1, 2)
plt.plot(z_centerline_fit, y_display, "ro")
plt.plot(z_centerline_fit, y_centerline_fit)
plt.xlabel("Z")
plt.ylabel("Y")
plt.title("y and y_fit coordinates")
plt.show()
# Create an image with the centerline
for iz in range(min_z_index, max_z_index + 1):
data[
round(x_centerline_fit[iz - min_z_index]), round(y_centerline_fit[iz - min_z_index]), iz
] = (
1
) # if index is out of bounds here for hanning: either the segmentation has holes or labels have been added to the file
# Write the centerline image in RPI orientation
hdr.set_data_dtype("uint8") # set imagetype to uint8
sct.printv("\nWrite NIFTI volumes...", verbose)
img = nibabel.Nifti1Image(data, None, hdr)
nibabel.save(img, "centerline.nii.gz")
sct.generate_output_file("centerline.nii.gz", file_data + "_centerline" + ext_data, verbose)
# create a txt file with the centerline
file_name = file_data + "_centerline" + ".txt"
sct.printv("\nWrite text file...", verbose)
file_results = open(file_name, "w")
for i in range(min_z_index, max_z_index + 1):
file_results.write(
str(int(i))
+ " "
+ str(x_centerline_fit[i - min_z_index])
+ " "
+ str(y_centerline_fit[i - min_z_index])
+ "\n"
)
file_results.close()
# Copy result into parent folder
sct.run("cp " + file_name + " ../")
del data
# come back to parent folder
os.chdir("..")
# Change orientation of the output centerline into input orientation
sct.printv("\nOrient centerline image to input orientation: " + orientation, verbose)
fname_segmentation_orient = "tmp.segmentation_rpi" + ext_data
set_orientation(
path_tmp + "/" + file_data + "_centerline" + ext_data, orientation, file_data + "_centerline" + ext_data
)
# Remove temporary files
if remove_temp_files:
sct.printv("\nRemove temporary files...", verbose)
sct.run("rm -rf " + path_tmp, verbose)
return file_data + "_centerline" + ext_data
def main():
# Initialization
fname_anat = ''
fname_centerline = ''
centerline_fitting = 'polynome'
remove_temp_files = param.remove_temp_files
interp = param.interp
degree_poly = param.deg_poly
# extract path of the script
path_script = os.path.dirname(__file__) + '/'
# Parameters for debug mode
if param.debug == 1:
print '\n*** WARNING: DEBUG MODE ON ***\n'
status, path_sct_data = commands.getstatusoutput(
'echo $SCT_TESTING_DATA_DIR')
fname_anat = path_sct_data + '/t2/t2.nii.gz'
fname_centerline = path_sct_data + '/t2/t2_seg.nii.gz'
else:
# Check input param
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:c:r:d:f:s:')
except getopt.GetoptError as err:
print str(err)
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_anat = arg
elif opt in ('-c'):
fname_centerline = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ('-d'):
degree_poly = int(arg)
elif opt in ('-f'):
centerline_fitting = str(arg)
elif opt in ('-s'):
interp = str(arg)
# display usage if a mandatory argument is not provided
if fname_anat == '' or fname_centerline == '':
usage()
# check existence of input files
sct.check_file_exist(fname_anat)
sct.check_file_exist(fname_centerline)
# extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
# Display arguments
print '\nCheck input arguments...'
print ' Input volume ...................... ' + fname_anat
print ' Centerline ........................ ' + fname_centerline
print ''
# Get input image orientation
input_image_orientation = get_orientation(fname_anat)
# Reorient input data into RL PA IS orientation
set_orientation(fname_anat, 'RPI', 'tmp.anat_orient.nii')
set_orientation(fname_centerline, 'RPI', 'tmp.centerline_orient.nii')
# Open centerline
#==========================================================================================
print '\nGet dimensions of input centerline...'
nx, ny, nz, nt, px, py, pz, pt = Image('tmp.centerline_orient.nii').dim
print '.. matrix size: ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)
print '.. voxel size: ' + str(px) + 'mm x ' + str(py) + 'mm x ' + str(
pz) + 'mm'
print '\nOpen centerline volume...'
file = nibabel.load('tmp.centerline_orient.nii')
data = file.get_data()
X, Y, Z = (data > 0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
# loop across z and associate x,y coordinate with the point having maximum intensity
x_centerline = [0 for iz in range(min_z_index, max_z_index + 1, 1)]
y_centerline = [0 for iz in range(min_z_index, max_z_index + 1, 1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index + 1, 1)]
# Two possible scenario:
# 1. the centerline is probabilistic: each slices contains voxels with the probability of containing the centerline [0:...:1]
# We only take the maximum value of the image to aproximate the centerline.
# 2. The centerline/segmentation image contains many pixels per slice with values {0,1}.
# We take all the points and approximate the centerline on all these points.
X, Y, Z = ((data < 1) * (data > 0)).nonzero() # X is empty if binary image
if (len(X) > 0): # Scenario 1
for iz in range(min_z_index, max_z_index + 1, 1):
x_centerline[iz - min_z_index], y_centerline[
iz - min_z_index] = numpy.unravel_index(
data[:, :, iz].argmax(), data[:, :, iz].shape)
else: # Scenario 2
for iz in range(min_z_index, max_z_index + 1, 1):
x_seg, y_seg = (data[:, :, iz] > 0).nonzero()
if len(x_seg) > 0:
x_centerline[iz - min_z_index] = numpy.mean(x_seg)
y_centerline[iz - min_z_index] = numpy.mean(y_seg)
# TODO: find a way to do the previous loop with this, which is more neat:
# [numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape) for iz in range(0,nz,1)]
# clear variable
del data
# Fit the centerline points with the kind of curve given as argument of the script and return the new smoothed coordinates
if centerline_fitting == 'splines':
try:
x_centerline_fit, y_centerline_fit = b_spline_centerline(
x_centerline, y_centerline, z_centerline)
except ValueError:
print "splines fitting doesn't work, trying with polynomial fitting...\n"
x_centerline_fit, y_centerline_fit = polynome_centerline(
x_centerline, y_centerline, z_centerline)
elif centerline_fitting == 'polynome':
x_centerline_fit, y_centerline_fit = polynome_centerline(
x_centerline, y_centerline, z_centerline)
#==========================================================================================
# Split input volume
print '\nSplit input volume...'
from sct_split_data import split_data
if not split_data('tmp.anat_orient.nii', 2, '_z'):
sct.printv('ERROR in split_data.', 1, 'error')
file_anat_split = [
'tmp.anat_orient_z' + str(z).zfill(4) for z in range(0, nz, 1)
]
# initialize variables
file_mat_inv_cumul = [
'tmp.mat_inv_cumul_z' + str(z).zfill(4) for z in range(0, nz, 1)
]
z_init = min_z_index
displacement_max_z_index = x_centerline_fit[
z_init - min_z_index] - x_centerline_fit[max_z_index - min_z_index]
# write centerline as text file
print '\nGenerate fitted transformation matrices...'
file_mat_inv_cumul_fit = [
'tmp.mat_inv_cumul_fit_z' + str(z).zfill(4) for z in range(0, nz, 1)
]
for iz in range(min_z_index, max_z_index + 1, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], 'w')
if (x_centerline[iz - min_z_index] == 0
and y_centerline[iz - min_z_index] == 0):
displacement = 0
else:
displacement = x_centerline_fit[
z_init - min_z_index] - x_centerline_fit[iz - min_z_index]
fid.write('%i %i %i %f\n' % (1, 0, 0, displacement))
fid.write('%i %i %i %f\n' % (0, 1, 0, 0))
fid.write('%i %i %i %i\n' % (0, 0, 1, 0))
fid.write('%i %i %i %i\n' % (0, 0, 0, 1))
fid.close()
# we complete the displacement matrix in z direction
for iz in range(0, min_z_index, 1):
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' % (1, 0, 0, 0))
fid.write('%i %i %i %f\n' % (0, 1, 0, 0))
fid.write('%i %i %i %i\n' % (0, 0, 1, 0))
fid.write('%i %i %i %i\n' % (0, 0, 0, 1))
fid.close()
for iz in range(max_z_index + 1, nz, 1):
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' % (1, 0, 0, displacement_max_z_index))
fid.write('%i %i %i %f\n' % (0, 1, 0, 0))
fid.write('%i %i %i %i\n' % (0, 0, 1, 0))
fid.write('%i %i %i %i\n' % (0, 0, 0, 1))
fid.close()
# apply transformations to data
print '\nApply fitted transformation matrices...'
file_anat_split_fit = [
'tmp.anat_orient_fit_z' + str(z).zfill(4) for z in range(0, nz, 1)
]
for iz in range(0, nz, 1):
# forward cumulative transformation to data
sct.run(fsloutput + 'flirt -in ' + file_anat_split[iz] + ' -ref ' +
file_anat_split[iz] + ' -applyxfm -init ' +
file_mat_inv_cumul_fit[iz] + ' -out ' +
file_anat_split_fit[iz] + ' -interp ' + interp)
# Merge into 4D volume
print '\nMerge into 4D volume...'
from sct_concat_data import concat_data
from glob import glob
concat_data(glob('tmp.anat_orient_fit_z*.nii'),
'tmp.anat_orient_fit.nii',
dim=2)
# sct.run(fsloutput+'fslmerge -z tmp.anat_orient_fit tmp.anat_orient_fit_z*')
# Reorient data as it was before
print '\nReorient data back into native orientation...'
set_orientation('tmp.anat_orient_fit.nii', input_image_orientation,
'tmp.anat_orient_fit_reorient.nii')
# Generate output file (in current folder)
print '\nGenerate output file (in current folder)...'
sct.generate_output_file('tmp.anat_orient_fit_reorient.nii',
file_anat + '_flatten' + ext_anat)
# Delete temporary files
if remove_temp_files == 1:
print '\nDelete temporary files...'
sct.run('rm -rf tmp.*')
# to view results
print '\nDone! To view results, type:'
print 'fslview ' + file_anat + ext_anat + ' ' + file_anat + '_flatten' + ext_anat + ' &\n'
def main(list_file,
param,
output_file_name=None,
remove_temp_files=1,
verbose=0):
path, file, ext = sct.extract_fname(list_file[0])
# create temporary folder
path_tmp = 'tmp.' + time.strftime("%y%m%d%H%M%S")
sct.run('mkdir ' + path_tmp)
# copy files into tmp folder
sct.printv('\nCopy files into tmp folder...', verbose)
for i in range(len(list_file)):
file_temp = os.path.abspath(list_file[i])
sct.run('cp ' + file_temp + ' ' + path_tmp)
# go to tmp folder
os.chdir(path_tmp)
## Concatenation of the files
# Concatenation : sum of matrices
file_0 = load(file + ext)
data_concatenation = file_0.get_data()
hdr_0 = file_0.get_header()
orientation_file_0 = get_orientation(list_file[0])
if len(list_file) > 0:
for i in range(1, len(list_file)):
orientation_file_temp = get_orientation(list_file[i])
if orientation_file_0 != orientation_file_temp:
print "ERROR: The files ", list_file[0], " and ", list_file[
i], " are not in the same orientation. Use sct_orientation to change the orientation of a file."
sys.exit(2)
file_temp = load(list_file[i])
data_temp = file_temp.get_data()
data_concatenation = data_concatenation + data_temp
# Save concatenation as a file
print '\nWrite NIFTI volumes...'
img = Nifti1Image(data_concatenation, None, hdr_0)
save(img, 'concatenation_file.nii.gz')
# Applying nurbs to the concatenation and save file as binary file
fname_output = extract_centerline('concatenation_file.nii.gz',
remove_temp_files=remove_temp_files,
verbose=verbose,
algo_fitting=param.algo_fitting,
type_window=param.type_window,
window_length=param.window_length)
# Rename files after processing
if output_file_name != None:
output_file_name = output_file_name
else:
output_file_name = "generated_centerline.nii.gz"
os.rename(fname_output, output_file_name)
path_binary, file_binary, ext_binary = sct.extract_fname(output_file_name)
os.rename('concatenation_file_centerline.txt', file_binary + '.txt')
# Process for a binary file as output:
sct.run('cp ' + output_file_name + ' ../')
# Process for a text file as output:
sct.run('cp ' + file_binary + '.txt' + ' ../')
os.chdir('../')
# Remove temporary files
if remove_temp_files:
print('\nRemove temporary files...')
sct.run('rm -rf ' + path_tmp)
def create_mask():
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# display usage if a mandatory argument is not provided
if param.fname_data == '' or param.method == '':
sct.printv('\nERROR: All mandatory arguments are not provided. See usage (add -h).\n', 1, 'error')
# parse argument for method
method_list = param.method.replace(' ', '').split(',') # remove spaces and parse with comma
# method_list = param.method.split(',') # parse with comma
method_type = method_list[0]
# check existence of method type
if not method_type in param.method_list:
sct.printv('\nERROR in '+os.path.basename(__file__)+': Method "'+method_type+'" is not recognized. See usage (add -h).\n', 1, 'error')
# check method val
if not method_type == 'center':
method_val = method_list[1]
del method_list
# check existence of shape
if not param.shape in param.shape_list:
sct.printv('\nERROR in '+os.path.basename(__file__)+': Shape "'+param.shape+'" is not recognized. See usage (add -h).\n', 1, 'error')
# check existence of input files
sct.printv('\ncheck existence of input files...', param.verbose)
sct.check_file_exist(param.fname_data, param.verbose)
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# check if orientation is RPI
if not get_orientation(param.fname_data) == 'RPI':
sct.printv('\nERROR in '+os.path.basename(__file__)+': Orientation of input image should be RPI. Use sct_orientation to put your image in RPI.\n', 1, 'error')
# display input parameters
sct.printv('\nInput parameters:', param.verbose)
sct.printv(' data ..................'+param.fname_data, param.verbose)
sct.printv(' method ................'+method_type, param.verbose)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
# Get output folder and file name
if param.fname_out == '':
param.fname_out = param.file_prefix+file_data+ext_data
#fname_out = os.path.abspath(path_out+file_out+ext_out)
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir '+path_tmp, param.verbose)
# Copying input data to tmp folder and convert to nii
# NB: cannot use c3d here because c3d cannot convert 4D data.
sct.printv('\nCopying input data to tmp folder and convert to nii...', param.verbose)
sct.run('cp '+param.fname_data+' '+path_tmp+'data'+ext_data, param.verbose)
if method_type == 'centerline':
sct.run('isct_c3d '+method_val+' -o '+path_tmp+'/centerline.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# convert to nii format
sct.run('fslchfiletype NIFTI data', param.verbose)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension('data.nii')
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), param.verbose)
# in case user input 4d data
if nt != 1:
sct.printv('WARNING in '+os.path.basename(__file__)+': Input image is 4d but output mask will 3D.', param.verbose, 'warning')
# extract first volume to have 3d reference
sct.run(fsloutput+'fslroi data data -0 1', param.verbose)
if method_type == 'coord':
# parse to get coordinate
coord = map(int, method_val.split('x'))
if method_type == 'point':
# get file name
fname_point = method_val
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
status, output = sct.run('sct_label_utils -i '+fname_point+' -t display-voxel', param.verbose)
# parse to get coordinate
coord = output[output.find('Position=')+10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = round(float(nx)/2), round(float(ny)/2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline.nii.gz'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line('data.nii', coord, nz)
# create mask
sct.printv('\nCreate mask...', param.verbose)
centerline = nibabel.load(fname_centerline) # open centerline
hdr = centerline.get_header() # get header
hdr.set_data_dtype('uint8') # set imagetype to uint8
data_centerline = centerline.get_data() # get centerline
z_centerline = [iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()]
nz = len(z_centerline)
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(numpy.array(data_centerline[:, :, z_centerline[iz]]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
center = numpy.array([cx[iz], cy[iz]])
mask2d = create_mask2d(center, param.shape, param.size, nx, ny)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask+str(iz)+'.nii'))
# merge along Z
cmd = 'fslmerge -z mask '
for iz in range(nz):
cmd = cmd + file_mask+str(iz)+' '
status, output = sct.run(cmd, param.verbose)
# copy geometry
sct.run(fsloutput+'fslcpgeom data mask', param.verbose)
# sct.run('fslchfiletype NIFTI mask', param.verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(path_tmp+'mask.nii.gz', param.fname_out)
# Remove temporary files
if param.remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf '+path_tmp, param.verbose)
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview '+param.fname_data+' '+param.fname_out+' -l Red -t 0.5 &', param.verbose, 'info')
print
def compute_csa(fname_segmentation, name_method, volume_output, verbose, remove_temp_files, step, smoothing_param, figure_fit, name_output, slices, vert_levels, path_to_template, algo_fitting = 'hanning', type_window = 'hanning', window_length = 80):
# Extract path, file and extension
fname_segmentation = os.path.abspath(fname_segmentation)
path_data, file_data, ext_data = sct.extract_fname(fname_segmentation)
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir '+path_tmp, verbose)
# Copying input data to tmp folder and convert to nii
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.run('isct_c3d '+fname_segmentation+' -o '+path_tmp+'segmentation.nii')
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input segmentation into RPI
sct.printv('\nChange orientation of the input segmentation into RPI...', verbose)
fname_segmentation_orient = set_orientation('segmentation.nii', 'RPI', 'segmentation_orient.nii')
# Get size of data
sct.printv('\nGet data dimensions...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_segmentation_orient).dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# Open segmentation volume
sct.printv('\nOpen segmentation volume...', verbose)
file_seg = nibabel.load(fname_segmentation_orient)
data_seg = file_seg.get_data()
hdr_seg = file_seg.get_header()
# # Extract min and max index in Z direction
X, Y, Z = (data_seg > 0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
# Xp, Yp = (data_seg[:, :, 0] >= 0).nonzero() # X and Y range
# extract centerline and smooth it
x_centerline_fit, y_centerline_fit, z_centerline, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = smooth_centerline(fname_segmentation_orient, algo_fitting=algo_fitting, type_window=type_window, window_length=window_length, verbose=verbose)
z_centerline_scaled = [x*pz for x in z_centerline]
# Compute CSA
sct.printv('\nCompute CSA...', verbose)
# Empty arrays in which CSA for each z slice will be stored
csa = np.zeros(max_z_index-min_z_index+1)
# csa = [0.0 for i in xrange(0, max_z_index-min_z_index+1)]
for iz in xrange(0, len(z_centerline)):
# compute the vector normal to the plane
normal = normalize(np.array([x_centerline_deriv[iz], y_centerline_deriv[iz], z_centerline_deriv[iz]]))
# compute the angle between the normal vector of the plane and the vector z
angle = np.arccos(np.dot(normal, [0, 0, 1]))
# compute the number of voxels, assuming the segmentation is coded for partial volume effect between 0 and 1.
number_voxels = sum(sum(data_seg[:, :, iz+min_z_index]))
# compute CSA, by scaling with voxel size (in mm) and adjusting for oblique plane
csa[iz] = number_voxels * px * py * np.cos(angle)
if smoothing_param:
from msct_smooth import smoothing_window
sct.printv('\nSmooth CSA across slices...', verbose)
sct.printv('.. Hanning window: '+str(smoothing_param)+' mm', verbose)
csa_smooth = smoothing_window(csa, window_len=smoothing_param/pz, window='hanning', verbose=0)
# display figure
if verbose == 2:
import matplotlib.pyplot as plt
plt.figure()
pltx, = plt.plot(z_centerline_scaled, csa, 'bo')
pltx_fit, = plt.plot(z_centerline_scaled, csa_smooth, 'r', linewidth=2)
plt.title("Cross-sectional area (CSA)")
plt.xlabel('z (mm)')
plt.ylabel('CSA (mm^2)')
plt.legend([pltx, pltx_fit], ['Raw', 'Smoothed'])
plt.show()
# update variable
csa = csa_smooth
# Create output text file
sct.printv('\nWrite text file...', verbose)
file_results = open('csa.txt', 'w')
for i in range(min_z_index, max_z_index+1):
file_results.write(str(int(i)) + ',' + str(csa[i-min_z_index])+'\n')
# Display results
sct.printv('z='+str(i-min_z_index)+': '+str(csa[i-min_z_index])+' mm^2', verbose, 'bold')
file_results.close()
# output volume of csa values
if volume_output:
sct.printv('\nCreate volume of CSA values...', verbose)
# get orientation of the input data
orientation = get_orientation('segmentation.nii')
data_seg = data_seg.astype(np.float32, copy=False)
# loop across slices
for iz in range(min_z_index, max_z_index+1):
# retrieve seg pixels
x_seg, y_seg = (data_seg[:, :, iz] > 0).nonzero()
seg = [[x_seg[i],y_seg[i]] for i in range(0, len(x_seg))]
# loop across pixels in segmentation
for i in seg:
# replace value with csa value
data_seg[i[0], i[1], iz] = csa[iz-min_z_index]
# create header
hdr_seg.set_data_dtype('float32') # set imagetype to uint8
# save volume
img = nibabel.Nifti1Image(data_seg, None, hdr_seg)
nibabel.save(img, 'csa_RPI.nii')
# Change orientation of the output centerline into input orientation
fname_csa_volume = set_orientation('csa_RPI.nii', orientation, 'csa_RPI_orient.nii')
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
from shutil import copyfile
copyfile(path_tmp+'csa.txt', path_data+param.fname_csa)
# sct.generate_output_file(path_tmp+'csa.txt', path_data+param.fname_csa) # extension already included in param.fname_csa
if volume_output:
sct.generate_output_file(fname_csa_volume, path_data+name_output) # extension already included in name_output
# average csa across vertebral levels or slices if asked (flag -z or -l)
if slices or vert_levels:
if vert_levels and not path_to_template:
sct.printv('\nERROR: Path to template is missing. See usage.\n', 1, 'error')
sys.exit(2)
elif vert_levels and path_to_template:
abs_path_to_template = os.path.abspath(path_to_template)
# go to tmp folder
os.chdir(path_tmp)
# create temporary folder
sct.printv('\nCreate temporary folder to average csa...', verbose)
path_tmp_extract_metric = sct.slash_at_the_end('label_temp', 1)
sct.run('mkdir '+path_tmp_extract_metric, verbose)
# Copying output CSA volume in the temporary folder
sct.printv('\nCopy data to tmp folder...', verbose)
sct.run('cp '+fname_segmentation+' '+path_tmp_extract_metric)
# create file info_label
path_fname_seg, file_fname_seg, ext_fname_seg = sct.extract_fname(fname_segmentation)
create_info_label('info_label.txt', path_tmp_extract_metric, file_fname_seg+ext_fname_seg)
# average CSA
if slices:
os.system("sct_extract_metric -i "+path_data+name_output+" -f "+path_tmp_extract_metric+" -m wa -o ../csa_mean.txt -z "+slices)
if vert_levels:
sct.run('cp -R '+abs_path_to_template+' .')
os.system("sct_extract_metric -i "+path_data+name_output+" -f "+path_tmp_extract_metric+" -m wa -o ../csa_mean.txt -v "+vert_levels)
os.chdir('..')
# Remove temporary files
print('\nRemove temporary folder used to average CSA...')
sct.run('rm -rf '+path_tmp_extract_metric)
# Remove temporary files
if remove_temp_files:
print('\nRemove temporary files...')
sct.run('rm -rf '+path_tmp)
def main():
# Initialization
fname_anat = ''
fname_point = ''
slice_gap = param.gap
remove_tmp_files = param.remove_tmp_files
gaussian_kernel = param.gaussian_kernel
start_time = time.time()
# get path of the toolbox
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
path_sct = sct.slash_at_the_end(path_sct, 1)
# Parameters for debug mode
if param.debug == 1:
sct.printv('\n*** WARNING: DEBUG MODE ON ***\n\t\t\tCurrent working directory: '+os.getcwd(), 'warning')
status, path_sct_testing_data = commands.getstatusoutput('echo $SCT_TESTING_DATA_DIR')
fname_anat = path_sct_testing_data+'/t2/t2.nii.gz'
fname_point = path_sct_testing_data+'/t2/t2_centerline_init.nii.gz'
slice_gap = 5
else:
# Check input param
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:p:g:r:k:')
except getopt.GetoptError as err:
print str(err)
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_anat = arg
elif opt in ('-p'):
fname_point = arg
elif opt in ('-g'):
slice_gap = int(arg)
elif opt in ('-r'):
remove_tmp_files = int(arg)
elif opt in ('-k'):
gaussian_kernel = int(arg)
# display usage if a mandatory argument is not provided
if fname_anat == '' or fname_point == '':
usage()
# check existence of input files
sct.check_file_exist(fname_anat)
sct.check_file_exist(fname_point)
# extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_point, file_point, ext_point = sct.extract_fname(fname_point)
# extract path of schedule file
# TODO: include schedule file in sct
# TODO: check existence of schedule file
file_schedule = path_sct + param.schedule_file
# Get input image orientation
input_image_orientation = get_orientation(fname_anat)
# Display arguments
print '\nCheck input arguments...'
print ' Anatomical image: '+fname_anat
print ' Orientation: '+input_image_orientation
print ' Point in spinal cord: '+fname_point
print ' Slice gap: '+str(slice_gap)
print ' Gaussian kernel: '+str(gaussian_kernel)
print ' Degree of polynomial: '+str(param.deg_poly)
# create temporary folder
print('\nCreate temporary folder...')
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")
sct.create_folder(path_tmp)
print '\nCopy input data...'
sct.run('cp '+fname_anat+ ' '+path_tmp+'/tmp.anat'+ext_anat)
sct.run('cp '+fname_point+ ' '+path_tmp+'/tmp.point'+ext_point)
# go to temporary folder
os.chdir(path_tmp)
# convert to nii
sct.run('fslchfiletype NIFTI tmp.anat')
sct.run('fslchfiletype NIFTI tmp.point')
# Reorient input anatomical volume into RL PA IS orientation
print '\nReorient input volume to RL PA IS orientation...'
#sct.run(sct.fsloutput + 'fslswapdim tmp.anat RL PA IS tmp.anat_orient')
set_orientation('tmp.anat.nii', 'RPI', 'tmp.anat_orient.nii')
# Reorient binary point into RL PA IS orientation
print '\nReorient binary point into RL PA IS orientation...'
sct.run(sct.fsloutput + 'fslswapdim tmp.point RL PA IS tmp.point_orient')
set_orientation('tmp.point.nii', 'RPI', 'tmp.point_orient')
# Get image dimensions
print '\nGet image dimensions...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension('tmp.anat_orient')
print '.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz)
print '.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm'
# Split input volume
print '\nSplit input volume...'
sct.run(sct.fsloutput + 'fslsplit tmp.anat_orient tmp.anat_orient_z -z')
file_anat_split = ['tmp.anat_orient_z'+str(z).zfill(4) for z in range(0,nz,1)]
# Get the coordinates of the input point
print '\nGet the coordinates of the input point...'
file = nibabel.load('tmp.point_orient.nii')
data = file.get_data()
x_init, y_init, z_init = (data > 0).nonzero()
x_init = x_init[0]
y_init = y_init[0]
z_init = z_init[0]
print '('+str(x_init)+', '+str(y_init)+', '+str(z_init)+')'
# Extract the slice corresponding to z=z_init
print '\nExtract the slice corresponding to z='+str(z_init)+'...'
file_point_split = ['tmp.point_orient_z'+str(z).zfill(4) for z in range(0,nz,1)]
sct.run(sct.fsloutput+'fslroi tmp.point_orient '+file_point_split[z_init]+' 0 -1 0 -1 '+str(z_init)+' 1')
# Create gaussian mask from point
print '\nCreate gaussian mask from point...'
file_mask_split = ['tmp.mask_orient_z'+str(z).zfill(4) for z in range(0,nz,1)]
sct.run(sct.fsloutput+'fslmaths '+file_point_split[z_init]+' -s '+str(gaussian_kernel)+' '+file_mask_split[z_init])
# Obtain max value from mask
print '\nFind maximum value from mask...'
file = nibabel.load(file_mask_split[z_init]+'.nii')
data = file.get_data()
max_value_mask = numpy.max(data)
print '..'+str(max_value_mask)
# Normalize mask beween 0 and 1
print '\nNormalize mask beween 0 and 1...'
sct.run(sct.fsloutput+'fslmaths '+file_mask_split[z_init]+' -div '+str(max_value_mask)+' '+file_mask_split[z_init])
## Take the square of the mask
#print '\nCalculate the square of the mask...'
#sct.run(sct.fsloutput+'fslmaths '+file_mask_split[z_init]+' -mul '+file_mask_split[z_init]+' '+file_mask_split[z_init])
# initialize variables
file_mat = ['tmp.mat_z'+str(z).zfill(4) for z in range(0,nz,1)]
file_mat_inv = ['tmp.mat_inv_z'+str(z).zfill(4) for z in range(0,nz,1)]
file_mat_inv_cumul = ['tmp.mat_inv_cumul_z'+str(z).zfill(4) for z in range(0,nz,1)]
# create identity matrix for initial transformation matrix
fid = open(file_mat_inv_cumul[z_init], 'w')
fid.write('%i %i %i %i\n' %(1, 0, 0, 0) )
fid.write('%i %i %i %i\n' %(0, 1, 0, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
# initialize centerline: give value corresponding to initial point
x_centerline = [x_init]
y_centerline = [y_init]
z_centerline = [z_init]
warning_count = 0
# go up (1), then down (2) in reference to the binary point
for iUpDown in range(1, 3):
if iUpDown == 1:
# z increases
slice_gap_signed = slice_gap
elif iUpDown == 2:
# z decreases
slice_gap_signed = -slice_gap
# reverse centerline (because values will be appended at the end)
x_centerline.reverse()
y_centerline.reverse()
z_centerline.reverse()
# initialization before looping
z_dest = z_init # point given by user
z_src = z_dest + slice_gap_signed
# continue looping if 0 < z < nz
while 0 <= z_src and z_src <= nz-1:
# print current z:
print 'z='+str(z_src)+':'
# estimate transformation
sct.run(fsloutput+'flirt -in '+file_anat_split[z_src]+' -ref '+file_anat_split[z_dest]+' -schedule '+file_schedule+ ' -verbose 0 -omat '+file_mat[z_src]+' -cost normcorr -forcescaling -inweight '+file_mask_split[z_dest]+' -refweight '+file_mask_split[z_dest])
# display transfo
status, output = sct.run('cat '+file_mat[z_src])
print output
# check if transformation is bigger than 1.5x slice_gap
tx = float(output.split()[3])
ty = float(output.split()[7])
norm_txy = numpy.linalg.norm([tx, ty],ord=2)
if norm_txy > 1.5*slice_gap:
print 'WARNING: Transformation is too large --> using previous one.'
warning_count = warning_count + 1
# if previous transformation exists, replace current one with previous one
if os.path.isfile(file_mat[z_dest]):
sct.run('cp '+file_mat[z_dest]+' '+file_mat[z_src])
# estimate inverse transformation matrix
sct.run('convert_xfm -omat '+file_mat_inv[z_src]+' -inverse '+file_mat[z_src])
# compute cumulative transformation
sct.run('convert_xfm -omat '+file_mat_inv_cumul[z_src]+' -concat '+file_mat_inv[z_src]+' '+file_mat_inv_cumul[z_dest])
# apply inverse cumulative transformation to initial gaussian mask (to put it in src space)
sct.run(fsloutput+'flirt -in '+file_mask_split[z_init]+' -ref '+file_mask_split[z_init]+' -applyxfm -init '+file_mat_inv_cumul[z_src]+' -out '+file_mask_split[z_src])
# open inverse cumulative transformation file and generate centerline
fid = open(file_mat_inv_cumul[z_src])
mat = fid.read().split()
x_centerline.append(x_init + float(mat[3]))
y_centerline.append(y_init + float(mat[7]))
z_centerline.append(z_src)
#z_index = z_index+1
# define new z_dest (target slice) and new z_src (moving slice)
z_dest = z_dest + slice_gap_signed
z_src = z_src + slice_gap_signed
# Reconstruct centerline
# ====================================================================================================
# reverse back centerline (because it's been reversed once, so now all values are in the right order)
x_centerline.reverse()
y_centerline.reverse()
z_centerline.reverse()
# fit centerline in the Z-X plane using polynomial function
print '\nFit centerline in the Z-X plane using polynomial function...'
coeffsx = numpy.polyfit(z_centerline, x_centerline, deg=param.deg_poly)
polyx = numpy.poly1d(coeffsx)
x_centerline_fit = numpy.polyval(polyx, z_centerline)
# calculate RMSE
rmse = numpy.linalg.norm(x_centerline_fit-x_centerline)/numpy.sqrt( len(x_centerline) )
# calculate max absolute error
max_abs = numpy.max( numpy.abs(x_centerline_fit-x_centerline) )
print '.. RMSE (in mm): '+str(rmse*px)
print '.. Maximum absolute error (in mm): '+str(max_abs*px)
# fit centerline in the Z-Y plane using polynomial function
print '\nFit centerline in the Z-Y plane using polynomial function...'
coeffsy = numpy.polyfit(z_centerline, y_centerline, deg=param.deg_poly)
polyy = numpy.poly1d(coeffsy)
y_centerline_fit = numpy.polyval(polyy, z_centerline)
# calculate RMSE
rmse = numpy.linalg.norm(y_centerline_fit-y_centerline)/numpy.sqrt( len(y_centerline) )
# calculate max absolute error
max_abs = numpy.max( numpy.abs(y_centerline_fit-y_centerline) )
print '.. RMSE (in mm): '+str(rmse*py)
print '.. Maximum absolute error (in mm): '+str(max_abs*py)
# display
if param.debug == 1:
import matplotlib.pyplot as plt
plt.figure()
plt.plot(z_centerline,x_centerline,'.',z_centerline,x_centerline_fit,'r')
plt.legend(['Data','Polynomial Fit'])
plt.title('Z-X plane polynomial interpolation')
plt.show()
plt.figure()
plt.plot(z_centerline,y_centerline,'.',z_centerline,y_centerline_fit,'r')
plt.legend(['Data','Polynomial Fit'])
plt.title('Z-Y plane polynomial interpolation')
plt.show()
# generate full range z-values for centerline
z_centerline_full = [iz for iz in range(0, nz, 1)]
# calculate X and Y values for the full centerline
x_centerline_fit_full = numpy.polyval(polyx, z_centerline_full)
y_centerline_fit_full = numpy.polyval(polyy, z_centerline_full)
# Generate fitted transformation matrices and write centerline coordinates in text file
print '\nGenerate fitted transformation matrices and write centerline coordinates in text file...'
file_mat_inv_cumul_fit = ['tmp.mat_inv_cumul_fit_z'+str(z).zfill(4) for z in range(0,nz,1)]
file_mat_cumul_fit = ['tmp.mat_cumul_fit_z'+str(z).zfill(4) for z in range(0,nz,1)]
fid_centerline = open('tmp.centerline_coordinates.txt', 'w')
for iz in range(0, nz, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' %(1, 0, 0, x_centerline_fit_full[iz]-x_init) )
fid.write('%i %i %i %f\n' %(0, 1, 0, y_centerline_fit_full[iz]-y_init) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
# compute forward cumulative fitted transformation matrix
sct.run('convert_xfm -omat '+file_mat_cumul_fit[iz]+' -inverse '+file_mat_inv_cumul_fit[iz])
# write centerline coordinates in x, y, z format
fid_centerline.write('%f %f %f\n' %(x_centerline_fit_full[iz], y_centerline_fit_full[iz], z_centerline_full[iz]) )
fid_centerline.close()
# Prepare output data
# ====================================================================================================
# write centerline as text file
for iz in range(0, nz, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' %(1, 0, 0, x_centerline_fit_full[iz]-x_init) )
fid.write('%i %i %i %f\n' %(0, 1, 0, y_centerline_fit_full[iz]-y_init) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
# write polynomial coefficients
numpy.savetxt('tmp.centerline_polycoeffs_x.txt',coeffsx)
numpy.savetxt('tmp.centerline_polycoeffs_y.txt',coeffsy)
# apply transformations to data
print '\nApply fitted transformation matrices...'
file_anat_split_fit = ['tmp.anat_orient_fit_z'+str(z).zfill(4) for z in range(0,nz,1)]
file_mask_split_fit = ['tmp.mask_orient_fit_z'+str(z).zfill(4) for z in range(0,nz,1)]
file_point_split_fit = ['tmp.point_orient_fit_z'+str(z).zfill(4) for z in range(0,nz,1)]
for iz in range(0, nz, 1):
# forward cumulative transformation to data
sct.run(fsloutput+'flirt -in '+file_anat_split[iz]+' -ref '+file_anat_split[iz]+' -applyxfm -init '+file_mat_cumul_fit[iz]+' -out '+file_anat_split_fit[iz])
# inverse cumulative transformation to mask
sct.run(fsloutput+'flirt -in '+file_mask_split[z_init]+' -ref '+file_mask_split[z_init]+' -applyxfm -init '+file_mat_inv_cumul_fit[iz]+' -out '+file_mask_split_fit[iz])
# inverse cumulative transformation to point
sct.run(fsloutput+'flirt -in '+file_point_split[z_init]+' -ref '+file_point_split[z_init]+' -applyxfm -init '+file_mat_inv_cumul_fit[iz]+' -out '+file_point_split_fit[iz]+' -interp nearestneighbour')
# Merge into 4D volume
print '\nMerge into 4D volume...'
sct.run(fsloutput+'fslmerge -z tmp.anat_orient_fit tmp.anat_orient_fit_z*')
sct.run(fsloutput+'fslmerge -z tmp.mask_orient_fit tmp.mask_orient_fit_z*')
sct.run(fsloutput+'fslmerge -z tmp.point_orient_fit tmp.point_orient_fit_z*')
# Copy header geometry from input data
print '\nCopy header geometry from input data...'
sct.run(fsloutput+'fslcpgeom tmp.anat_orient.nii tmp.anat_orient_fit.nii ')
sct.run(fsloutput+'fslcpgeom tmp.anat_orient.nii tmp.mask_orient_fit.nii ')
sct.run(fsloutput+'fslcpgeom tmp.anat_orient.nii tmp.point_orient_fit.nii ')
# Reorient outputs into the initial orientation of the input image
print '\nReorient the centerline into the initial orientation of the input image...'
set_orientation('tmp.point_orient_fit.nii', input_image_orientation, 'tmp.point_orient_fit.nii')
set_orientation('tmp.mask_orient_fit.nii', input_image_orientation, 'tmp.mask_orient_fit.nii')
# Generate output file (in current folder)
print '\nGenerate output file (in current folder)...'
os.chdir('..') # come back to parent folder
#sct.generate_output_file('tmp.centerline_polycoeffs_x.txt','./','centerline_polycoeffs_x','.txt')
#sct.generate_output_file('tmp.centerline_polycoeffs_y.txt','./','centerline_polycoeffs_y','.txt')
#sct.generate_output_file('tmp.centerline_coordinates.txt','./','centerline_coordinates','.txt')
#sct.generate_output_file('tmp.anat_orient.nii','./',file_anat+'_rpi',ext_anat)
#sct.generate_output_file('tmp.anat_orient_fit.nii', file_anat+'_rpi_align'+ext_anat)
#sct.generate_output_file('tmp.mask_orient_fit.nii', file_anat+'_mask'+ext_anat)
fname_output_centerline = sct.generate_output_file(path_tmp+'/tmp.point_orient_fit.nii', file_anat+'_centerline'+ext_anat)
# Delete temporary files
if remove_tmp_files == 1:
print '\nRemove temporary files...'
sct.run('rm -rf '+path_tmp)
# print number of warnings
print '\nNumber of warnings: '+str(warning_count)+' (if >10, you should probably reduce the gap and/or increase the kernel size'
# display elapsed time
elapsed_time = time.time() - start_time
print '\nFinished! \n\tGenerated file: '+fname_output_centerline+'\n\tElapsed time: '+str(int(round(elapsed_time)))+'s\n'
def compute_csa(fname_segmentation, name_method, volume_output, verbose, remove_temp_files, spline_smoothing, step, smoothing_param, figure_fit, name_output, slices, vert_levels, path_to_template, algo_fitting = 'hanning', type_window = 'hanning', window_length = 80):
#param.algo_fitting = 'hanning'
# Extract path, file and extension
fname_segmentation = os.path.abspath(fname_segmentation)
path_data, file_data, ext_data = sct.extract_fname(fname_segmentation)
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir '+path_tmp, verbose)
# Copying input data to tmp folder and convert to nii
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.run('isct_c3d '+fname_segmentation+' -o '+path_tmp+'segmentation.nii')
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input segmentation into RPI
sct.printv('\nChange orientation of the input segmentation into RPI...', verbose)
fname_segmentation_orient = set_orientation('segmentation.nii', 'RPI', 'segmentation_orient.nii')
# Get size of data
sct.printv('\nGet data dimensions...', verbose)
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_segmentation_orient)
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# Open segmentation volume
sct.printv('\nOpen segmentation volume...', verbose)
file_seg = nibabel.load(fname_segmentation_orient)
data_seg = file_seg.get_data()
hdr_seg = file_seg.get_header()
#
# # Extract min and max index in Z direction
X, Y, Z = (data_seg > 0).nonzero()
# coords_seg = np.array([str([X[i], Y[i], Z[i]]) for i in xrange(0,len(Z))]) # don't know why but finding strings in array of array of strings is WAY faster than doing the same with integers
min_z_index, max_z_index = min(Z), max(Z)
Xp,Yp = (data_seg[:,:,0]>=0).nonzero() # X and Y range
#
# x_centerline = [0 for i in xrange(0,max_z_index-min_z_index+1)]
# y_centerline = [0 for i in xrange(0,max_z_index-min_z_index+1)]
# z_centerline = np.array([iz for iz in xrange(min_z_index, max_z_index+1)])
#
# # Extract segmentation points and average per slice
# for iz in xrange(min_z_index, max_z_index+1):
# x_seg, y_seg = (data_seg[:,:,iz]>0).nonzero()
# x_centerline[iz-min_z_index] = np.mean(x_seg)
# y_centerline[iz-min_z_index] = np.mean(y_seg)
#
# # Fit the centerline points with spline and return the new fitted coordinates
# x_centerline_fit, y_centerline_fit,x_centerline_deriv,y_centerline_deriv,z_centerline_deriv = b_spline_centerline(x_centerline,y_centerline,z_centerline)
# extract centerline and smooth it
x_centerline_fit, y_centerline_fit, z_centerline, x_centerline_deriv,y_centerline_deriv,z_centerline_deriv = smooth_centerline(fname_segmentation_orient, algo_fitting=algo_fitting, type_window=type_window, window_length=window_length, verbose = verbose)
z_centerline_scaled = [x*pz for x in z_centerline]
# # 3D plot of the fit
# fig=plt.figure()
# ax=Axes3D(fig)
# ax.plot(x_centerline,y_centerline,z_centerline,zdir='z')
# ax.plot(x_centerline_fit,y_centerline_fit,z_centerline,zdir='z')
# plt.show()
# Defining cartesian basis vectors
x = np.array([1, 0, 0])
y = np.array([0, 1, 0])
z = np.array([0, 0, 1])
# Creating folder in which JPG files will be stored
sct.printv('\nCreating folder in which JPG files will be stored...', verbose)
sct.create_folder('JPG_Results')
# Compute CSA
sct.printv('\nCompute CSA...', verbose)
# Empty arrays in which CSA for each z slice will be stored
csa = [0.0 for i in xrange(0,max_z_index-min_z_index+1)]
# sections_ortho_counting = [0 for i in xrange(0,max_z_index-min_z_index+1)]
# sections_ortho_ellipse = [0 for i in xrange(0,max_z_index-min_z_index+1)]
# sections_z_ellipse = [0 for i in xrange(0,max_z_index-min_z_index+1)]
# sections_z_counting = [0 for i in xrange(0,max_z_index-min_z_index+1)]
sct.printv('\nCross-Section Area:', verbose, 'bold')
for iz in xrange(0, len(z_centerline)):
# Equation of the the plane which is orthogonal to the spline at z=iz
a = x_centerline_deriv[iz]
b = y_centerline_deriv[iz]
c = z_centerline_deriv[iz]
#vector normal to the plane
normal = normalize(np.array([a, b, c]))
# angle between normal vector and z
angle = np.arccos(np.dot(normal, z))
if name_method == 'counting_ortho_plane' or name_method == 'ellipse_ortho_plane':
x_center = x_centerline_fit[iz]
y_center = y_centerline_fit[iz]
z_center = z_centerline[iz]
# use of x in order to get orientation of each plane, basis_1 is in the plane ax+by+cz+d=0
basis_1 = normalize(np.cross(normal,x))
basis_2 = normalize(np.cross(normal,basis_1))
# maximum dimension of the tilted plane. Try multiply numerator by sqrt(2) ?
max_diameter = (max([(max(X)-min(X))*px,(max(Y)-min(Y))*py]))/(np.cos(angle))
# Forcing the step to be the min of x and y scale (default value is 1 mm)
step = min([px,py])
# discretized plane which will be filled with 0/1
plane_seg = np.zeros((int(max_diameter/step),int(max_diameter/step)))
# how the plane will be skimmed through
plane_grid = np.linspace(-int(max_diameter/2),int(max_diameter/2),int(max_diameter/step))
# we go through the plane
for i_b1 in plane_grid :
for i_b2 in plane_grid :
point = np.array([x_center*px,y_center*py,z_center*pz]) + i_b1*basis_1 +i_b2*basis_2
# to which voxel belongs each point of the plane
coord_voxel = str([ int(point[0]/px), int(point[1]/py), int(point[2]/pz)])
if (coord_voxel in coords_seg) is True : # if this voxel is 1
plane_seg[int((plane_grid==i_b1).nonzero()[0])][int((plane_grid==i_b2).nonzero()[0])] = 1
# number of voxels that are in the intersection of each plane and the nonzeros values of segmentation, times the area of one cell of the discretized plane
if name_method == 'counting_ortho_plane':
csa[iz] = len((plane_seg>0).nonzero()[0])*step*step
# if verbose ==1 and name_method == 'counting_ortho_plane' :
# print('Cross-Section Area : ' + str(csa[iz]) + ' mm^2')
if name_method == 'ellipse_ortho_plane':
# import scipy stuff
from scipy.misc import imsave
os.chdir('JPG_Results')
imsave('plane_ortho_' + str(iz) + '.jpg', plane_seg)
# Tresholded gradient image
mag = edge_detection('plane_ortho_' + str(iz) + '.jpg')
#Coordinates of the contour
x_contour,y_contour = (mag>0).nonzero()
x_contour = x_contour*step
y_contour = y_contour*step
#Fitting an ellipse
fit = Ellipse_fit(x_contour,y_contour)
# Semi-minor axis, semi-major axis
a_ellipse, b_ellipse = ellipse_dim(fit)
#Section = pi*a*b
csa[iz] = a_ellipse*b_ellipse*np.pi
# if verbose == 1 and name_method == 'ellipse_ortho_plane':
# print('Cross-Section Area : ' + str(csa[iz]) + ' mm^2')
# os.chdir('..')
if name_method == 'counting_z_plane' or name_method == 'ellipse_z_plane':
# getting the segmentation for each z plane
x_seg, y_seg = (data_seg[:, :, iz+min_z_index] > 0).nonzero()
seg = [[x_seg[i], y_seg[i]] for i in range(0, len(x_seg))]
plane = np.zeros((max(Xp), max(Yp)))
for i in seg:
# filling the plane with 0 and 1 regarding to the segmentation
plane[i[0] - 1][i[1] - 1] = data_seg[i[0] - 1, i[1] - 1, iz+min_z_index]
if name_method == 'counting_z_plane':
x, y = (plane > 0.0).nonzero()
len_x = len(x)
for i in range(0, len_x):
csa[iz] += plane[x[i], y[i]]*px*py
csa[iz] *= np.cos(angle)
# if verbose == 1 and name_method == 'counting_z_plane':
# print('Cross-Section Area : ' + str(csa[iz]) + ' mm^2')
if name_method == 'ellipse_z_plane':
# import scipy stuff
from scipy.misc import imsave
os.chdir('JPG_Results')
imsave('plane_z_' + str(iz) + '.jpg', plane)
# Tresholded gradient image
mag = edge_detection('plane_z_' + str(iz) + '.jpg')
x_contour,y_contour = (mag>0).nonzero()
x_contour = x_contour*px
y_contour = y_contour*py
# Fitting an ellipse
fit = Ellipse_fit(x_contour,y_contour)
a_ellipse, b_ellipse = ellipse_dim(fit)
csa[iz] = a_ellipse*b_ellipse*np.pi*np.cos(angle)
# if verbose == 1 and name_method == 'ellipse_z_plane':
# print('Cross-Section Area : ' + str(csa[iz]) + ' mm^2')
if spline_smoothing == 1:
sct.printv('\nSmoothing results with spline...', verbose)
tck = scipy.interpolate.splrep(z_centerline_scaled, csa, s=smoothing_param)
csa_smooth = scipy.interpolate.splev(z_centerline_scaled, tck)
if figure_fit == 1:
import matplotlib.pyplot as plt
plt.figure()
plt.plot(z_centerline_scaled, csa)
plt.plot(z_centerline_scaled, csa_smooth)
plt.legend(['CSA values', 'Smoothed values'], 2)
plt.savefig('Spline_fit.png')
csa = csa_smooth # update variable
# Create output text file
sct.printv('\nWrite text file...', verbose)
file_results = open('csa.txt', 'w')
for i in range(min_z_index, max_z_index+1):
file_results.write(str(int(i)) + ',' + str(csa[i-min_z_index])+'\n')
# Display results
sct.printv('z='+str(i-min_z_index)+': '+str(csa[i-min_z_index])+' mm^2', verbose, 'bold')
file_results.close()
# output volume of csa values
if volume_output:
sct.printv('\nCreate volume of CSA values...', verbose)
# get orientation of the input data
orientation = get_orientation('segmentation.nii')
data_seg = data_seg.astype(np.float32, copy=False)
# loop across slices
for iz in range(min_z_index, max_z_index+1):
# retrieve seg pixels
x_seg, y_seg = (data_seg[:, :, iz] > 0).nonzero()
seg = [[x_seg[i],y_seg[i]] for i in range(0, len(x_seg))]
# loop across pixels in segmentation
for i in seg:
# replace value with csa value
data_seg[i[0], i[1], iz] = csa[iz-min_z_index]
# create header
hdr_seg.set_data_dtype('float32') # set imagetype to uint8
# save volume
img = nibabel.Nifti1Image(data_seg, None, hdr_seg)
nibabel.save(img, 'csa_RPI.nii')
# Change orientation of the output centerline into input orientation
fname_csa_volume = set_orientation('csa_RPI.nii', orientation, 'csa_RPI_orient.nii')
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp+'csa.txt', path_data+param.fname_csa) # extension already included in param.fname_csa
if volume_output:
sct.generate_output_file(fname_csa_volume, path_data+name_output) # extension already included in name_output
# average csa across vertebral levels or slices if asked (flag -z or -l)
if slices or vert_levels:
if vert_levels and not path_to_template:
sct.printv('\nERROR: Path to template is missing. See usage.\n', 1, 'error')
sys.exit(2)
elif vert_levels and path_to_template:
abs_path_to_template = os.path.abspath(path_to_template)
# go to tmp folder
os.chdir(path_tmp)
# create temporary folder
sct.printv('\nCreate temporary folder to average csa...', verbose)
path_tmp_extract_metric = sct.slash_at_the_end('label_temp', 1)
sct.run('mkdir '+path_tmp_extract_metric, verbose)
# Copying output CSA volume in the temporary folder
sct.printv('\nCopy data to tmp folder...', verbose)
sct.run('cp '+fname_segmentation+' '+path_tmp_extract_metric)
# create file info_label
path_fname_seg, file_fname_seg, ext_fname_seg = sct.extract_fname(fname_segmentation)
create_info_label('info_label.txt', path_tmp_extract_metric, file_fname_seg+ext_fname_seg)
if slices:
# average CSA
os.system("sct_extract_metric -i "+path_data+name_output+" -f "+path_tmp_extract_metric+" -m wa -o "+sct.slash_at_the_end(path_data)+"mean_csa -z "+slices)
if vert_levels:
sct.run('cp -R '+abs_path_to_template+' .')
# average CSA
os.system("sct_extract_metric -i "+path_data+name_output+" -f "+path_tmp_extract_metric+" -m wa -o "+sct.slash_at_the_end(path_data)+"mean_csa -v "+vert_levels)
os.chdir('..')
# Remove temporary files
print('\nRemove temporary folder used to average CSA...')
sct.run('rm -rf '+path_tmp_extract_metric)
# Remove temporary files
if remove_temp_files:
print('\nRemove temporary files...')
sct.run('rm -rf '+path_tmp)
def main(segmentation_file=None,
label_file=None,
output_file_name=None,
parameter="binary_centerline",
remove_temp_files=1,
verbose=0):
#Process for a binary file as output:
if parameter == "binary_centerline":
# Binary_centerline: Process for only a segmentation file:
if "-i" in arguments and "-l" not in arguments:
# Extract path, file and extension
segmentation_file = os.path.abspath(segmentation_file)
path_data, file_data, ext_data = sct.extract_fname(
segmentation_file)
# create temporary folder
path_tmp = 'tmp.' + time.strftime("%y%m%d%H%M%S")
sct.run('mkdir ' + path_tmp)
# copy files into tmp folder
sct.run('cp ' + segmentation_file + ' ' + path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input segmentation into RPI
print '\nOrient segmentation image to RPI orientation...'
fname_segmentation_orient = 'tmp.segmentation_rpi' + ext_data
set_orientation(file_data + ext_data, 'RPI',
fname_segmentation_orient)
# Extract orientation of the input segmentation
orientation = get_orientation(file_data + ext_data)
print '\nOrientation of segmentation image: ' + orientation
# Get size of data
print '\nGet dimensions data...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(
fname_segmentation_orient)
print '.. ' + str(nx) + ' x ' + str(ny) + ' y ' + str(
nz) + ' z ' + str(nt)
print '\nOpen segmentation volume...'
file = nibabel.load(fname_segmentation_orient)
data = file.get_data()
hdr = file.get_header()
# Extract min and max index in Z direction
X, Y, Z = (data > 0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
x_centerline = [0 for i in range(0, max_z_index - min_z_index + 1)]
y_centerline = [0 for i in range(0, max_z_index - min_z_index + 1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index + 1)]
# Extract segmentation points and average per slice
for iz in range(min_z_index, max_z_index + 1):
x_seg, y_seg = (data[:, :, iz] > 0).nonzero()
x_centerline[iz - min_z_index] = np.mean(x_seg)
y_centerline[iz - min_z_index] = np.mean(y_seg)
#ne sert a rien
for k in range(len(X)):
data[X[k], Y[k], Z[k]] = 0
print len(x_centerline)
# Fit the centerline points with splines and return the new fitted coordinates
#done with nurbs for now
x_centerline_fit, y_centerline_fit, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = b_spline_centerline(
x_centerline, y_centerline, z_centerline)
# Create an image with the centerline
for iz in range(min_z_index, max_z_index + 1):
data[round(x_centerline_fit[iz - min_z_index]),
round(y_centerline_fit[iz - min_z_index]),
iz] = 1 #with nurbs fitting
#data[round(x_centerline[iz-min_z_index]), round(y_centerline[iz-min_z_index]), iz] = 1 #without nurbs fitting
# Write the centerline image in RPI orientation
hdr.set_data_dtype('uint8') # set imagetype to uint8
print '\nWrite NIFTI volumes...'
img = nibabel.Nifti1Image(data, None, hdr)
if output_file_name != None:
file_name = output_file_name
else:
file_name = file_data + '_centerline' + ext_data
nibabel.save(img, 'tmp.centerline.nii')
sct.generate_output_file('tmp.centerline.nii', file_name)
del data
# come back to parent folder
os.chdir('..')
# Change orientation of the output centerline into input orientation
print '\nOrient centerline image to input orientation: ' + orientation
set_orientation(path_tmp + '/' + file_name, orientation, file_name)
# Remove temporary files
if remove_temp_files:
print('\nRemove temporary files...')
sct.run('rm -rf ' + path_tmp)
return file_name
# Binary_centerline: Process for only a label file:
if "-l" in arguments and "-i" not in arguments:
file = os.path.abspath(label_file)
path_data, file_data, ext_data = sct.extract_fname(file)
file = nibabel.load(label_file)
data = file.get_data()
hdr = file.get_header()
X, Y, Z = (data > 0).nonzero()
Z_new = np.linspace(min(Z), max(Z), (max(Z) - min(Z) + 1))
# sort X and Y arrays using Z
X = [X[i] for i in Z[:].argsort()]
Y = [Y[i] for i in Z[:].argsort()]
Z = [Z[i] for i in Z[:].argsort()]
#print X, Y, Z
f1 = interpolate.UnivariateSpline(Z, X)
f2 = interpolate.UnivariateSpline(Z, Y)
X_fit = f1(Z_new)
Y_fit = f2(Z_new)
#print X_fit
#print Y_fit
if verbose == 1:
import matplotlib.pyplot as plt
plt.figure()
plt.plot(Z_new, X_fit)
plt.plot(Z, X, 'o', linestyle='None')
plt.show()
plt.figure()
plt.plot(Z_new, Y_fit)
plt.plot(Z, Y, 'o', linestyle='None')
plt.show()
data = data * 0
for i in xrange(len(X_fit)):
data[X_fit[i], Y_fit[i], Z_new[i]] = 1
# Create NIFTI image
print '\nSave volume ...'
hdr.set_data_dtype('float32') # set image type to uint8
img = nibabel.Nifti1Image(data, None, hdr)
if output_file_name != None:
file_name = output_file_name
else:
file_name = file_data + '_centerline' + ext_data
# save volume
nibabel.save(img, file_name)
print '\nFile created : ' + file_name
del data
#### Binary_centerline: Process for a segmentation file and a label file:
if "-l" and "-i" in arguments:
## Creation of a temporary file that will contain each centerline file of the process
path_tmp = 'tmp.' + time.strftime("%y%m%d%H%M%S")
sct.run('mkdir ' + path_tmp)
##From label file create centerline image
print '\nPROCESS PART 1: From label file create centerline image.'
file_label = os.path.abspath(label_file)
path_data_label, file_data_label, ext_data_label = sct.extract_fname(
file_label)
file_label = nibabel.load(label_file)
#Copy label_file into temporary folder
sct.run('cp ' + label_file + ' ' + path_tmp)
data_label = file_label.get_data()
hdr_label = file_label.get_header()
if verbose == 1:
from copy import copy
data_label_to_show = copy(data_label)
X, Y, Z = (data_label > 0).nonzero()
Z_new = np.linspace(min(Z), max(Z), (max(Z) - min(Z) + 1))
# sort X and Y arrays using Z
X = [X[i] for i in Z[:].argsort()]
Y = [Y[i] for i in Z[:].argsort()]
Z = [Z[i] for i in Z[:].argsort()]
#print X, Y, Z
f1 = interpolate.UnivariateSpline(Z, X)
f2 = interpolate.UnivariateSpline(Z, Y)
X_fit = f1(Z_new)
Y_fit = f2(Z_new)
#print X_fit
#print Y_fit
if verbose == 1:
import matplotlib.pyplot as plt
plt.figure()
plt.plot(Z_new, X_fit)
plt.plot(Z, X, 'o', linestyle='None')
plt.show()
plt.figure()
plt.plot(Z_new, Y_fit)
plt.plot(Z, Y, 'o', linestyle='None')
plt.show()
data_label = data_label * 0
for i in xrange(len(X_fit)):
data_label[X_fit[i], Y_fit[i], Z_new[i]] = 1
# Create NIFTI image
print '\nSave volume ...'
hdr_label.set_data_dtype('float32') # set image type to uint8
img = nibabel.Nifti1Image(data_label, None, hdr_label)
# save volume
file_name_label = file_data_label + '_centerline' + ext_data_label
nibabel.save(img, file_name_label)
print '\nFile created : ' + file_name_label
# copy files into tmp folder
sct.run('cp ' + file_name_label + ' ' + path_tmp)
#effacer fichier dans folder parent
os.remove(file_name_label)
del data_label
##From segmentation file create centerline image
print '\nPROCESS PART 2: From segmentation file create centerline image.'
# Extract path, file and extension
segmentation_file = os.path.abspath(segmentation_file)
path_data_seg, file_data_seg, ext_data_seg = sct.extract_fname(
segmentation_file)
# copy files into tmp folder
sct.run('cp ' + segmentation_file + ' ' + path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input segmentation into RPI
print '\nOrient segmentation image to RPI orientation...'
fname_segmentation_orient = 'tmp.segmentation_rpi' + ext_data_seg
set_orientation(file_data_seg + ext_data_seg, 'RPI',
fname_segmentation_orient)
# Extract orientation of the input segmentation
orientation = get_orientation(file_data_seg + ext_data_seg)
print '\nOrientation of segmentation image: ' + orientation
# Get size of data
print '\nGet dimensions data...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(
fname_segmentation_orient)
print '.. ' + str(nx) + ' x ' + str(ny) + ' y ' + str(
nz) + ' z ' + str(nt)
print '\nOpen segmentation volume...'
file_seg = nibabel.load(fname_segmentation_orient)
data_seg = file_seg.get_data()
hdr_seg = file_seg.get_header()
if verbose == 1:
data_seg_to_show = copy(data_seg)
# Extract min and max index in Z direction
X, Y, Z = (data_seg > 0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
x_centerline = [0 for i in range(0, max_z_index - min_z_index + 1)]
y_centerline = [0 for i in range(0, max_z_index - min_z_index + 1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index + 1)]
# Extract segmentation points and average per slice
for iz in range(min_z_index, max_z_index + 1):
x_seg, y_seg = (data_seg[:, :, iz] > 0).nonzero()
x_centerline[iz - min_z_index] = np.mean(x_seg)
y_centerline[iz - min_z_index] = np.mean(y_seg)
for k in range(len(X)):
data_seg[X[k], Y[k], Z[k]] = 0
# Fit the centerline points with splines and return the new fitted coordinates
#done with nurbs for now
x_centerline_fit, y_centerline_fit, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = b_spline_centerline(
x_centerline, y_centerline, z_centerline)
# Create an image with the centerline
for iz in range(min_z_index, max_z_index + 1):
data_seg[round(x_centerline_fit[iz - min_z_index]),
round(y_centerline_fit[iz - min_z_index]), iz] = 1
# Write the centerline image in RPI orientation
hdr_seg.set_data_dtype('uint8') # set imagetype to uint8
print '\nWrite NIFTI volumes...'
img = nibabel.Nifti1Image(data_seg, None, hdr_seg)
nibabel.save(img, 'tmp.centerline.nii')
file_name_seg = file_data_seg + '_centerline' + ext_data_seg
sct.generate_output_file('tmp.centerline.nii',
file_name_seg) #pb ici
# copy files into parent folder
#sct.run('cp '+file_name_seg+' ../')
del data_seg
# come back to parent folder
# os.chdir('..')
# Change orientation of the output centerline into input orientation
print '\nOrient centerline image to input orientation: ' + orientation
set_orientation(file_name_seg, orientation, file_name_seg)
print '\nRemoving overlap of the centerline obtain with label file if there are any:'
## Remove overlap from centerline file obtain with label file
remove_overlap(file_name_label, file_name_seg,
"generated_centerline_without_overlap.nii.gz")
## Concatenation of the two centerline files
print '\nConcatenation of the two centerline files:'
if output_file_name != None:
file_name = output_file_name
else:
file_name = 'centerline_total_from_label_and_seg'
sct.run(
'fslmaths generated_centerline_without_overlap.nii.gz -add ' +
file_name_seg + ' ' + file_name)
if verbose == 1:
import matplotlib.pyplot as plt
from scipy import ndimage
#Get back concatenation of segmentation and labels before any processing
data_concatenate = data_seg_to_show + data_label_to_show
z_centerline = [
iz for iz in range(0, nz, 1)
if data_concatenate[:, :, iz].any()
]
nz_nonz = len(z_centerline)
x_centerline = [0 for iz in range(0, nz_nonz, 1)]
y_centerline = [0 for iz in range(0, nz_nonz, 1)]
# Calculate centerline coordinates and create image of the centerline
for iz in range(0, nz_nonz, 1):
x_centerline[iz], y_centerline[
iz] = ndimage.measurements.center_of_mass(
data_concatenate[:, :, z_centerline[iz]])
#Load file with resulting centerline
file_centerline_fit = nibabel.load(file_name)
data_centerline_fit = file_centerline_fit.get_data()
z_centerline_fit = [
iz for iz in range(0, nz, 1)
if data_centerline_fit[:, :, iz].any()
]
nz_nonz_fit = len(z_centerline_fit)
x_centerline_fit_total = [0 for iz in range(0, nz_nonz_fit, 1)]
y_centerline_fit_total = [0 for iz in range(0, nz_nonz_fit, 1)]
#Convert to array
x_centerline_fit_total = np.asarray(x_centerline_fit_total)
y_centerline_fit_total = np.asarray(y_centerline_fit_total)
#Calculate overlap between seg and label
length_overlap = X_fit.shape[0] + x_centerline_fit.shape[
0] - x_centerline_fit_total.shape[0]
# The total fitting is the concatenation of the two fitting (
for i in range(x_centerline_fit.shape[0]):
x_centerline_fit_total[i] = x_centerline_fit[i]
y_centerline_fit_total[i] = y_centerline_fit[i]
for i in range(X_fit.shape[0] - length_overlap):
x_centerline_fit_total[x_centerline_fit.shape[0] +
i] = X_fit[i + length_overlap]
y_centerline_fit_total[x_centerline_fit.shape[0] +
i] = Y_fit[i + length_overlap]
print x_centerline_fit.shape[0] + i
#for iz in range(0, nz_nonz_fit, 1):
# x_centerline_fit[iz], y_centerline_fit[iz] = ndimage.measurements.center_of_mass(data_centerline_fit[:, :, z_centerline_fit[iz]])
#Creation of a vector x that takes into account the distance between the labels
#x_centerline_fit = np.asarray(x_centerline_fit)
#y_centerline_fit = np.asarray(y_centerline_fit)
x_display = [0 for i in range(x_centerline_fit_total.shape[0])]
y_display = [0 for i in range(y_centerline_fit_total.shape[0])]
for i in range(0, nz_nonz, 1):
x_display[z_centerline[i] -
z_centerline[0]] = x_centerline[i]
y_display[z_centerline[i] -
z_centerline[0]] = y_centerline[i]
plt.figure(1)
plt.subplot(2, 1, 1)
plt.plot(z_centerline_fit, x_display, 'ro')
plt.plot(z_centerline_fit, x_centerline_fit_total)
plt.xlabel("Z")
plt.ylabel("X")
plt.title("x and x_fit coordinates")
plt.subplot(2, 1, 2)
plt.plot(z_centerline_fit, y_display, 'ro')
plt.plot(z_centerline_fit, y_centerline_fit_total)
plt.xlabel("Z")
plt.ylabel("Y")
plt.title("y and y_fit coordinates")
plt.show()
del data_concatenate, data_label_to_show, data_seg_to_show, data_centerline_fit
sct.run('cp ' + file_name + ' ../')
# Copy result into parent folder
sct.run('cp ' + file_name + ' ../')
# Come back to parent folder
os.chdir('..')
# Remove temporary centerline files
if remove_temp_files:
print('\nRemove temporary files...')
sct.run('rm -rf ' + path_tmp)
#Process for a text file as output:
if parameter == "text_file":
print "\nText file process"
#Process for only a segmentation file:
if "-i" in arguments and "-l" not in arguments:
# Extract path, file and extension
segmentation_file = os.path.abspath(segmentation_file)
path_data, file_data, ext_data = sct.extract_fname(
segmentation_file)
# create temporary folder
path_tmp = 'tmp.' + time.strftime("%y%m%d%H%M%S")
sct.run('mkdir ' + path_tmp)
# copy files into tmp folder
sct.run('cp ' + segmentation_file + ' ' + path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input segmentation into RPI
print '\nOrient segmentation image to RPI orientation...'
fname_segmentation_orient = 'tmp.segmentation_rpi' + ext_data
set_orientation(file_data + ext_data, 'RPI',
fname_segmentation_orient)
# Extract orientation of the input segmentation
orientation = get_orientation(file_data + ext_data)
print '\nOrientation of segmentation image: ' + orientation
# Get size of data
print '\nGet dimensions data...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(
fname_segmentation_orient)
print '.. ' + str(nx) + ' x ' + str(ny) + ' y ' + str(
nz) + ' z ' + str(nt)
print '\nOpen segmentation volume...'
file = nibabel.load(fname_segmentation_orient)
data = file.get_data()
hdr = file.get_header()
# Extract min and max index in Z direction
X, Y, Z = (data > 0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
x_centerline = [0 for i in range(0, max_z_index - min_z_index + 1)]
y_centerline = [0 for i in range(0, max_z_index - min_z_index + 1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index + 1)]
# Extract segmentation points and average per slice
for iz in range(min_z_index, max_z_index + 1):
x_seg, y_seg = (data[:, :, iz] > 0).nonzero()
x_centerline[iz - min_z_index] = np.mean(x_seg)
y_centerline[iz - min_z_index] = np.mean(y_seg)
for k in range(len(X)):
data[X[k], Y[k], Z[k]] = 0
# Fit the centerline points with splines and return the new fitted coordinates
x_centerline_fit, y_centerline_fit, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = b_spline_centerline(
x_centerline, y_centerline, z_centerline)
# Create output text file
if output_file_name != None:
file_name = output_file_name
else:
file_name = file_data + '_centerline' + '.txt'
sct.printv('\nWrite text file...', verbose)
#file_results = open("../"+file_name, 'w')
file_results = open(file_name, 'w')
for i in range(min_z_index, max_z_index + 1):
file_results.write(
str(int(i)) + ' ' +
str(x_centerline_fit[i - min_z_index]) + ' ' +
str(y_centerline_fit[i - min_z_index]) + '\n')
file_results.close()
# Copy result into parent folder
sct.run('cp ' + file_name + ' ../')
del data
# come back to parent folder
os.chdir('..')
# Remove temporary files
if remove_temp_files:
print('\nRemove temporary files...')
sct.run('rm -rf ' + path_tmp)
return file_name
#Process for only a label file:
if "-l" in arguments and "-i" not in arguments:
file = os.path.abspath(label_file)
path_data, file_data, ext_data = sct.extract_fname(file)
file = nibabel.load(label_file)
data = file.get_data()
hdr = file.get_header()
X, Y, Z = (data > 0).nonzero()
Z_new = np.linspace(min(Z), max(Z), (max(Z) - min(Z) + 1))
# sort X and Y arrays using Z
X = [X[i] for i in Z[:].argsort()]
Y = [Y[i] for i in Z[:].argsort()]
Z = [Z[i] for i in Z[:].argsort()]
#print X, Y, Z
f1 = interpolate.UnivariateSpline(Z, X)
f2 = interpolate.UnivariateSpline(Z, Y)
X_fit = f1(Z_new)
Y_fit = f2(Z_new)
#print X_fit
#print Y_fit
if verbose == 1:
import matplotlib.pyplot as plt
plt.figure()
plt.plot(Z_new, X_fit)
plt.plot(Z, X, 'o', linestyle='None')
plt.show()
plt.figure()
plt.plot(Z_new, Y_fit)
plt.plot(Z, Y, 'o', linestyle='None')
plt.show()
data = data * 0
for iz in xrange(len(X_fit)):
data[X_fit[iz], Y_fit[iz], Z_new[iz]] = 1
# Create output text file
sct.printv('\nWrite text file...', verbose)
if output_file_name != None:
file_name = output_file_name
else:
file_name = file_data + '_centerline' + ext_data
file_results = open(file_name, 'w')
min_z_index, max_z_index = min(Z), max(Z)
for i in range(min_z_index, max_z_index + 1):
file_results.write(
str(int(i)) + ' ' + str(X_fit[i - min_z_index]) + ' ' +
str(Y_fit[i - min_z_index]) + '\n')
file_results.close()
del data
#Process for a segmentation file and a label file:
if "-l" and "-i" in arguments:
## Creation of a temporary file that will contain each centerline file of the process
path_tmp = 'tmp.' + time.strftime("%y%m%d%H%M%S")
sct.run('mkdir ' + path_tmp)
##From label file create centerline text file
print '\nPROCESS PART 1: From label file create centerline text file.'
file_label = os.path.abspath(label_file)
path_data_label, file_data_label, ext_data_label = sct.extract_fname(
file_label)
file_label = nibabel.load(label_file)
#Copy label_file into temporary folder
sct.run('cp ' + label_file + ' ' + path_tmp)
data_label = file_label.get_data()
hdr_label = file_label.get_header()
X, Y, Z = (data_label > 0).nonzero()
Z_new = np.linspace(min(Z), max(Z), (max(Z) - min(Z) + 1))
# sort X and Y arrays using Z
X = [X[i] for i in Z[:].argsort()]
Y = [Y[i] for i in Z[:].argsort()]
Z = [Z[i] for i in Z[:].argsort()]
#print X, Y, Z
f1 = interpolate.UnivariateSpline(Z, X)
f2 = interpolate.UnivariateSpline(Z, Y)
X_fit = f1(Z_new)
Y_fit = f2(Z_new)
#print X_fit
#print Y_fit
if verbose == 1:
import matplotlib.pyplot as plt
plt.figure()
plt.plot(Z_new, X_fit)
plt.plot(Z, X, 'o', linestyle='None')
plt.show()
plt.figure()
plt.plot(Z_new, Y_fit)
plt.plot(Z, Y, 'o', linestyle='None')
plt.show()
data_label = data_label * 0
for i in xrange(len(X_fit)):
data_label[X_fit[i], Y_fit[i], Z_new[i]] = 1
# Create output text file
sct.printv('\nWrite text file...', verbose)
file_name_label = file_data_label + '_centerline' + '.txt'
file_results = open(path_tmp + '/' + file_name_label, 'w')
min_z_index, max_z_index = min(Z), max(Z)
for i in range(min_z_index, max_z_index + 1):
file_results.write(
str(int(i)) + ' ' + str(X_fit[i - min_z_index]) + ' ' +
str(Y_fit[i - min_z_index]) + '\n')
file_results.close()
# copy files into tmp folder
#sct.run('cp '+file_name_label+' '+path_tmp)
del data_label
##From segmentation file create centerline text file
print '\nPROCESS PART 2: From segmentation file create centerline image.'
# Extract path, file and extension
segmentation_file = os.path.abspath(segmentation_file)
path_data_seg, file_data_seg, ext_data_seg = sct.extract_fname(
segmentation_file)
# copy files into tmp folder
sct.run('cp ' + segmentation_file + ' ' + path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input segmentation into RPI
print '\nOrient segmentation image to RPI orientation...'
fname_segmentation_orient = 'tmp.segmentation_rpi' + ext_data_seg
set_orientation(file_data_seg + ext_data_seg, 'RPI',
fname_segmentation_orient)
# Extract orientation of the input segmentation
orientation = get_orientation(file_data_seg + ext_data_seg)
print '\nOrientation of segmentation image: ' + orientation
# Get size of data
print '\nGet dimensions data...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(
fname_segmentation_orient)
print '.. ' + str(nx) + ' x ' + str(ny) + ' y ' + str(
nz) + ' z ' + str(nt)
print '\nOpen segmentation volume...'
file_seg = nibabel.load(fname_segmentation_orient)
data_seg = file_seg.get_data()
hdr_seg = file_seg.get_header()
# Extract min and max index in Z direction
X, Y, Z = (data_seg > 0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
x_centerline = [0 for i in range(0, max_z_index - min_z_index + 1)]
y_centerline = [0 for i in range(0, max_z_index - min_z_index + 1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index + 1)]
# Extract segmentation points and average per slice
for iz in range(min_z_index, max_z_index + 1):
x_seg, y_seg = (data_seg[:, :, iz] > 0).nonzero()
x_centerline[iz - min_z_index] = np.mean(x_seg)
y_centerline[iz - min_z_index] = np.mean(y_seg)
for k in range(len(X)):
data_seg[X[k], Y[k], Z[k]] = 0
# Fit the centerline points with splines and return the new fitted coordinates
#done with nurbs for now
x_centerline_fit, y_centerline_fit, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = b_spline_centerline(
x_centerline, y_centerline, z_centerline)
# Create output text file
file_name_seg = file_data_seg + '_centerline' + '.txt'
sct.printv('\nWrite text file...', verbose)
file_results = open(file_name_seg, 'w')
for i in range(min_z_index, max_z_index + 1):
file_results.write(
str(int(i)) + ' ' +
str(x_centerline_fit[i - min_z_index]) + ' ' +
str(y_centerline_fit[i - min_z_index]) + '\n')
file_results.close()
del data_seg
print '\nRemoving overlap of the centerline obtain with label file if there are any:'
## Remove overlap from centerline file obtain with label file
remove_overlap(file_name_label,
file_name_seg,
"generated_centerline_without_overlap1.txt",
parameter=1)
## Concatenation of the two centerline files
print '\nConcatenation of the two centerline files:'
if output_file_name != None:
file_name = output_file_name
else:
file_name = 'centerline_total_from_label_and_seg.txt'
f_output = open(file_name, "w")
f_output.close()
with open(file_name_seg, "r") as f_seg:
with open("generated_centerline_without_overlap1.txt",
"r") as f:
with open(file_name, "w") as f_output:
data_line_seg = f_seg.readlines()
data_line = f.readlines()
for line in data_line_seg:
f_output.write(line)
for line in data_line:
f_output.write(line)
# Copy result into parent folder
sct.run('cp ' + file_name + ' ../')
# Come back to parent folder
os.chdir('..')
# Remove temporary centerline files
if remove_temp_files:
print('\nRemove temporary files...')
sct.run('rm -rf ' + path_tmp)
def main(segmentation_file=None, label_file=None, output_file_name=None, parameter = "binary_centerline", remove_temp_files = 1, verbose = 0 ):
#Process for a binary file as output:
if parameter == "binary_centerline":
# Binary_centerline: Process for only a segmentation file:
if "-i" in arguments and "-l" not in arguments:
# Extract path, file and extension
segmentation_file = os.path.abspath(segmentation_file)
path_data, file_data, ext_data = sct.extract_fname(segmentation_file)
# create temporary folder
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")
sct.run('mkdir '+path_tmp)
# copy files into tmp folder
sct.run('cp '+segmentation_file+' '+path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input segmentation into RPI
print '\nOrient segmentation image to RPI orientation...'
fname_segmentation_orient = 'tmp.segmentation_rpi' + ext_data
set_orientation(file_data+ext_data, 'RPI', fname_segmentation_orient)
# Extract orientation of the input segmentation
orientation = get_orientation(file_data+ext_data)
print '\nOrientation of segmentation image: ' + orientation
# Get size of data
print '\nGet dimensions data...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_segmentation_orient)
print '.. '+str(nx)+' x '+str(ny)+' y '+str(nz)+' z '+str(nt)
print '\nOpen segmentation volume...'
file = nibabel.load(fname_segmentation_orient)
data = file.get_data()
hdr = file.get_header()
# Extract min and max index in Z direction
X, Y, Z = (data>0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
x_centerline = [0 for i in range(0,max_z_index-min_z_index+1)]
y_centerline = [0 for i in range(0,max_z_index-min_z_index+1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index+1)]
# Extract segmentation points and average per slice
for iz in range(min_z_index, max_z_index+1):
x_seg, y_seg = (data[:,:,iz]>0).nonzero()
x_centerline[iz-min_z_index] = np.mean(x_seg)
y_centerline[iz-min_z_index] = np.mean(y_seg)
#ne sert a rien
for k in range(len(X)):
data[X[k],Y[k],Z[k]] = 0
print len(x_centerline)
# Fit the centerline points with splines and return the new fitted coordinates
#done with nurbs for now
x_centerline_fit, y_centerline_fit,x_centerline_deriv,y_centerline_deriv,z_centerline_deriv = b_spline_centerline(x_centerline,y_centerline,z_centerline)
# Create an image with the centerline
for iz in range(min_z_index, max_z_index+1):
data[round(x_centerline_fit[iz-min_z_index]), round(y_centerline_fit[iz-min_z_index]), iz] = 1 #with nurbs fitting
#data[round(x_centerline[iz-min_z_index]), round(y_centerline[iz-min_z_index]), iz] = 1 #without nurbs fitting
# Write the centerline image in RPI orientation
hdr.set_data_dtype('uint8') # set imagetype to uint8
print '\nWrite NIFTI volumes...'
img = nibabel.Nifti1Image(data, None, hdr)
if output_file_name != None :
file_name = output_file_name
else: file_name = file_data+'_centerline'+ext_data
nibabel.save(img,'tmp.centerline.nii')
sct.generate_output_file('tmp.centerline.nii',file_name)
del data
# come back to parent folder
os.chdir('..')
# Change orientation of the output centerline into input orientation
print '\nOrient centerline image to input orientation: ' + orientation
set_orientation(path_tmp+'/'+file_name, orientation, file_name)
# Remove temporary files
if remove_temp_files:
print('\nRemove temporary files...')
sct.run('rm -rf '+path_tmp)
return file_name
# Binary_centerline: Process for only a label file:
if "-l" in arguments and "-i" not in arguments:
file = os.path.abspath(label_file)
path_data, file_data, ext_data = sct.extract_fname(file)
file = nibabel.load(label_file)
data = file.get_data()
hdr = file.get_header()
X,Y,Z = (data>0).nonzero()
Z_new = np.linspace(min(Z),max(Z),(max(Z)-min(Z)+1))
# sort X and Y arrays using Z
X = [X[i] for i in Z[:].argsort()]
Y = [Y[i] for i in Z[:].argsort()]
Z = [Z[i] for i in Z[:].argsort()]
#print X, Y, Z
f1 = interpolate.UnivariateSpline(Z, X)
f2 = interpolate.UnivariateSpline(Z, Y)
X_fit = f1(Z_new)
Y_fit = f2(Z_new)
#print X_fit
#print Y_fit
if verbose==1 :
import matplotlib.pyplot as plt
plt.figure()
plt.plot(Z_new,X_fit)
plt.plot(Z,X,'o',linestyle = 'None')
plt.show()
plt.figure()
plt.plot(Z_new,Y_fit)
plt.plot(Z,Y,'o',linestyle = 'None')
plt.show()
data =data*0
for i in xrange(len(X_fit)):
data[X_fit[i],Y_fit[i],Z_new[i]] = 1
# Create NIFTI image
print '\nSave volume ...'
hdr.set_data_dtype('float32') # set image type to uint8
img = nibabel.Nifti1Image(data, None, hdr)
if output_file_name != None :
file_name = output_file_name
else: file_name = file_data+'_centerline'+ext_data
# save volume
nibabel.save(img,file_name)
print '\nFile created : ' + file_name
del data
#### Binary_centerline: Process for a segmentation file and a label file:
if "-l" and "-i" in arguments:
## Creation of a temporary file that will contain each centerline file of the process
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")
sct.run('mkdir '+path_tmp)
##From label file create centerline image
print '\nPROCESS PART 1: From label file create centerline image.'
file_label = os.path.abspath(label_file)
path_data_label, file_data_label, ext_data_label = sct.extract_fname(file_label)
file_label = nibabel.load(label_file)
#Copy label_file into temporary folder
sct.run('cp '+label_file+' '+path_tmp)
data_label = file_label.get_data()
hdr_label = file_label.get_header()
if verbose == 1:
from copy import copy
data_label_to_show = copy(data_label)
X,Y,Z = (data_label>0).nonzero()
Z_new = np.linspace(min(Z),max(Z),(max(Z)-min(Z)+1))
# sort X and Y arrays using Z
X = [X[i] for i in Z[:].argsort()]
Y = [Y[i] for i in Z[:].argsort()]
Z = [Z[i] for i in Z[:].argsort()]
#print X, Y, Z
f1 = interpolate.UnivariateSpline(Z, X)
f2 = interpolate.UnivariateSpline(Z, Y)
X_fit = f1(Z_new)
Y_fit = f2(Z_new)
#print X_fit
#print Y_fit
if verbose==1 :
import matplotlib.pyplot as plt
plt.figure()
plt.plot(Z_new,X_fit)
plt.plot(Z,X,'o',linestyle = 'None')
plt.show()
plt.figure()
plt.plot(Z_new,Y_fit)
plt.plot(Z,Y,'o',linestyle = 'None')
plt.show()
data_label =data_label*0
for i in xrange(len(X_fit)):
data_label[X_fit[i],Y_fit[i],Z_new[i]] = 1
# Create NIFTI image
print '\nSave volume ...'
hdr_label.set_data_dtype('float32') # set image type to uint8
img = nibabel.Nifti1Image(data_label, None, hdr_label)
# save volume
file_name_label = file_data_label + '_centerline' + ext_data_label
nibabel.save(img, file_name_label)
print '\nFile created : ' + file_name_label
# copy files into tmp folder
sct.run('cp '+file_name_label+' '+path_tmp)
#effacer fichier dans folder parent
os.remove(file_name_label)
del data_label
##From segmentation file create centerline image
print '\nPROCESS PART 2: From segmentation file create centerline image.'
# Extract path, file and extension
segmentation_file = os.path.abspath(segmentation_file)
path_data_seg, file_data_seg, ext_data_seg = sct.extract_fname(segmentation_file)
# copy files into tmp folder
sct.run('cp '+segmentation_file+' '+path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input segmentation into RPI
print '\nOrient segmentation image to RPI orientation...'
fname_segmentation_orient = 'tmp.segmentation_rpi' + ext_data_seg
set_orientation(file_data_seg+ext_data_seg, 'RPI', fname_segmentation_orient)
# Extract orientation of the input segmentation
orientation = get_orientation(file_data_seg+ext_data_seg)
print '\nOrientation of segmentation image: ' + orientation
# Get size of data
print '\nGet dimensions data...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_segmentation_orient)
print '.. '+str(nx)+' x '+str(ny)+' y '+str(nz)+' z '+str(nt)
print '\nOpen segmentation volume...'
file_seg = nibabel.load(fname_segmentation_orient)
data_seg = file_seg.get_data()
hdr_seg = file_seg.get_header()
if verbose == 1:
data_seg_to_show = copy(data_seg)
# Extract min and max index in Z direction
X, Y, Z = (data_seg>0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
x_centerline = [0 for i in range(0,max_z_index-min_z_index+1)]
y_centerline = [0 for i in range(0,max_z_index-min_z_index+1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index+1)]
# Extract segmentation points and average per slice
for iz in range(min_z_index, max_z_index+1):
x_seg, y_seg = (data_seg[:,:,iz]>0).nonzero()
x_centerline[iz-min_z_index] = np.mean(x_seg)
y_centerline[iz-min_z_index] = np.mean(y_seg)
for k in range(len(X)):
data_seg[X[k],Y[k],Z[k]] = 0
# Fit the centerline points with splines and return the new fitted coordinates
#done with nurbs for now
x_centerline_fit, y_centerline_fit,x_centerline_deriv,y_centerline_deriv,z_centerline_deriv = b_spline_centerline(x_centerline,y_centerline,z_centerline)
# Create an image with the centerline
for iz in range(min_z_index, max_z_index+1):
data_seg[round(x_centerline_fit[iz-min_z_index]), round(y_centerline_fit[iz-min_z_index]), iz] = 1
# Write the centerline image in RPI orientation
hdr_seg.set_data_dtype('uint8') # set imagetype to uint8
print '\nWrite NIFTI volumes...'
img = nibabel.Nifti1Image(data_seg, None, hdr_seg)
nibabel.save(img,'tmp.centerline.nii')
file_name_seg = file_data_seg+'_centerline'+ext_data_seg
sct.generate_output_file('tmp.centerline.nii',file_name_seg) #pb ici
# copy files into parent folder
#sct.run('cp '+file_name_seg+' ../')
del data_seg
# come back to parent folder
# os.chdir('..')
# Change orientation of the output centerline into input orientation
print '\nOrient centerline image to input orientation: ' + orientation
set_orientation(file_name_seg, orientation, file_name_seg)
print '\nRemoving overlap of the centerline obtain with label file if there are any:'
## Remove overlap from centerline file obtain with label file
remove_overlap(file_name_label, file_name_seg, "generated_centerline_without_overlap.nii.gz")
## Concatenation of the two centerline files
print '\nConcatenation of the two centerline files:'
if output_file_name != None :
file_name = output_file_name
else: file_name = 'centerline_total_from_label_and_seg'
sct.run('fslmaths generated_centerline_without_overlap.nii.gz -add ' + file_name_seg + ' ' + file_name)
if verbose == 1 :
import matplotlib.pyplot as plt
from scipy import ndimage
#Get back concatenation of segmentation and labels before any processing
data_concatenate = data_seg_to_show + data_label_to_show
z_centerline = [iz for iz in range(0, nz, 1) if data_concatenate[:, :, iz].any()]
nz_nonz = len(z_centerline)
x_centerline = [0 for iz in range(0, nz_nonz, 1)]
y_centerline = [0 for iz in range(0, nz_nonz, 1)]
# Calculate centerline coordinates and create image of the centerline
for iz in range(0, nz_nonz, 1):
x_centerline[iz], y_centerline[iz] = ndimage.measurements.center_of_mass(data_concatenate[:, :, z_centerline[iz]])
#Load file with resulting centerline
file_centerline_fit = nibabel.load(file_name)
data_centerline_fit = file_centerline_fit.get_data()
z_centerline_fit = [iz for iz in range(0, nz, 1) if data_centerline_fit[:, :, iz].any()]
nz_nonz_fit = len(z_centerline_fit)
x_centerline_fit_total = [0 for iz in range(0, nz_nonz_fit, 1)]
y_centerline_fit_total = [0 for iz in range(0, nz_nonz_fit, 1)]
#Convert to array
x_centerline_fit_total = np.asarray(x_centerline_fit_total)
y_centerline_fit_total = np.asarray(y_centerline_fit_total)
#Calculate overlap between seg and label
length_overlap = X_fit.shape[0] + x_centerline_fit.shape[0] - x_centerline_fit_total.shape[0]
# The total fitting is the concatenation of the two fitting (
for i in range(x_centerline_fit.shape[0]):
x_centerline_fit_total[i] = x_centerline_fit[i]
y_centerline_fit_total[i] = y_centerline_fit[i]
for i in range(X_fit.shape[0]-length_overlap):
x_centerline_fit_total[x_centerline_fit.shape[0] + i] = X_fit[i+length_overlap]
y_centerline_fit_total[x_centerline_fit.shape[0] + i] = Y_fit[i+length_overlap]
print x_centerline_fit.shape[0] + i
#for iz in range(0, nz_nonz_fit, 1):
# x_centerline_fit[iz], y_centerline_fit[iz] = ndimage.measurements.center_of_mass(data_centerline_fit[:, :, z_centerline_fit[iz]])
#Creation of a vector x that takes into account the distance between the labels
#x_centerline_fit = np.asarray(x_centerline_fit)
#y_centerline_fit = np.asarray(y_centerline_fit)
x_display = [0 for i in range(x_centerline_fit_total.shape[0])]
y_display = [0 for i in range(y_centerline_fit_total.shape[0])]
for i in range(0, nz_nonz, 1):
x_display[z_centerline[i]-z_centerline[0]] = x_centerline[i]
y_display[z_centerline[i]-z_centerline[0]] = y_centerline[i]
plt.figure(1)
plt.subplot(2,1,1)
plt.plot(z_centerline_fit, x_display, 'ro')
plt.plot(z_centerline_fit, x_centerline_fit_total)
plt.xlabel("Z")
plt.ylabel("X")
plt.title("x and x_fit coordinates")
plt.subplot(2,1,2)
plt.plot(z_centerline_fit, y_display, 'ro')
plt.plot(z_centerline_fit, y_centerline_fit_total)
plt.xlabel("Z")
plt.ylabel("Y")
plt.title("y and y_fit coordinates")
plt.show()
del data_concatenate, data_label_to_show, data_seg_to_show, data_centerline_fit
sct.run('cp '+file_name+' ../')
# Copy result into parent folder
sct.run('cp '+file_name+' ../')
# Come back to parent folder
os.chdir('..')
# Remove temporary centerline files
if remove_temp_files:
print('\nRemove temporary files...')
sct.run('rm -rf '+path_tmp)
#Process for a text file as output:
if parameter == "text_file" :
print "\nText file process"
#Process for only a segmentation file:
if "-i" in arguments and "-l" not in arguments:
# Extract path, file and extension
segmentation_file = os.path.abspath(segmentation_file)
path_data, file_data, ext_data = sct.extract_fname(segmentation_file)
# create temporary folder
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")
sct.run('mkdir '+path_tmp)
# copy files into tmp folder
sct.run('cp '+segmentation_file+' '+path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input segmentation into RPI
print '\nOrient segmentation image to RPI orientation...'
fname_segmentation_orient = 'tmp.segmentation_rpi' + ext_data
set_orientation(file_data+ext_data, 'RPI', fname_segmentation_orient)
# Extract orientation of the input segmentation
orientation = get_orientation(file_data+ext_data)
print '\nOrientation of segmentation image: ' + orientation
# Get size of data
print '\nGet dimensions data...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_segmentation_orient)
print '.. '+str(nx)+' x '+str(ny)+' y '+str(nz)+' z '+str(nt)
print '\nOpen segmentation volume...'
file = nibabel.load(fname_segmentation_orient)
data = file.get_data()
hdr = file.get_header()
# Extract min and max index in Z direction
X, Y, Z = (data>0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
x_centerline = [0 for i in range(0,max_z_index-min_z_index+1)]
y_centerline = [0 for i in range(0,max_z_index-min_z_index+1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index+1)]
# Extract segmentation points and average per slice
for iz in range(min_z_index, max_z_index+1):
x_seg, y_seg = (data[:,:,iz]>0).nonzero()
x_centerline[iz-min_z_index] = np.mean(x_seg)
y_centerline[iz-min_z_index] = np.mean(y_seg)
for k in range(len(X)):
data[X[k],Y[k],Z[k]] = 0
# Fit the centerline points with splines and return the new fitted coordinates
x_centerline_fit, y_centerline_fit,x_centerline_deriv,y_centerline_deriv,z_centerline_deriv = b_spline_centerline(x_centerline,y_centerline,z_centerline)
# Create output text file
if output_file_name != None :
file_name = output_file_name
else: file_name = file_data+'_centerline'+'.txt'
sct.printv('\nWrite text file...', verbose)
#file_results = open("../"+file_name, 'w')
file_results = open(file_name, 'w')
for i in range(min_z_index, max_z_index+1):
file_results.write(str(int(i)) + ' ' + str(x_centerline_fit[i-min_z_index]) + ' ' + str(y_centerline_fit[i-min_z_index]) + '\n')
file_results.close()
# Copy result into parent folder
sct.run('cp '+file_name+' ../')
del data
# come back to parent folder
os.chdir('..')
# Remove temporary files
if remove_temp_files:
print('\nRemove temporary files...')
sct.run('rm -rf '+path_tmp)
return file_name
#Process for only a label file:
if "-l" in arguments and "-i" not in arguments:
file = os.path.abspath(label_file)
path_data, file_data, ext_data = sct.extract_fname(file)
file = nibabel.load(label_file)
data = file.get_data()
hdr = file.get_header()
X,Y,Z = (data>0).nonzero()
Z_new = np.linspace(min(Z),max(Z),(max(Z)-min(Z)+1))
# sort X and Y arrays using Z
X = [X[i] for i in Z[:].argsort()]
Y = [Y[i] for i in Z[:].argsort()]
Z = [Z[i] for i in Z[:].argsort()]
#print X, Y, Z
f1 = interpolate.UnivariateSpline(Z, X)
f2 = interpolate.UnivariateSpline(Z, Y)
X_fit = f1(Z_new)
Y_fit = f2(Z_new)
#print X_fit
#print Y_fit
if verbose==1 :
import matplotlib.pyplot as plt
plt.figure()
plt.plot(Z_new,X_fit)
plt.plot(Z,X,'o',linestyle = 'None')
plt.show()
plt.figure()
plt.plot(Z_new,Y_fit)
plt.plot(Z,Y,'o',linestyle = 'None')
plt.show()
data =data*0
for iz in xrange(len(X_fit)):
data[X_fit[iz],Y_fit[iz],Z_new[iz]] = 1
# Create output text file
sct.printv('\nWrite text file...', verbose)
if output_file_name != None :
file_name = output_file_name
else: file_name = file_data+'_centerline'+ext_data
file_results = open(file_name, 'w')
min_z_index, max_z_index = min(Z), max(Z)
for i in range(min_z_index, max_z_index+1):
file_results.write(str(int(i)) + ' ' + str(X_fit[i-min_z_index]) + ' ' + str(Y_fit[i-min_z_index]) + '\n')
file_results.close()
del data
#Process for a segmentation file and a label file:
if "-l" and "-i" in arguments:
## Creation of a temporary file that will contain each centerline file of the process
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")
sct.run('mkdir '+path_tmp)
##From label file create centerline text file
print '\nPROCESS PART 1: From label file create centerline text file.'
file_label = os.path.abspath(label_file)
path_data_label, file_data_label, ext_data_label = sct.extract_fname(file_label)
file_label = nibabel.load(label_file)
#Copy label_file into temporary folder
sct.run('cp '+label_file+' '+path_tmp)
data_label = file_label.get_data()
hdr_label = file_label.get_header()
X,Y,Z = (data_label>0).nonzero()
Z_new = np.linspace(min(Z),max(Z),(max(Z)-min(Z)+1))
# sort X and Y arrays using Z
X = [X[i] for i in Z[:].argsort()]
Y = [Y[i] for i in Z[:].argsort()]
Z = [Z[i] for i in Z[:].argsort()]
#print X, Y, Z
f1 = interpolate.UnivariateSpline(Z, X)
f2 = interpolate.UnivariateSpline(Z, Y)
X_fit = f1(Z_new)
Y_fit = f2(Z_new)
#print X_fit
#print Y_fit
if verbose==1 :
import matplotlib.pyplot as plt
plt.figure()
plt.plot(Z_new,X_fit)
plt.plot(Z,X,'o',linestyle = 'None')
plt.show()
plt.figure()
plt.plot(Z_new,Y_fit)
plt.plot(Z,Y,'o',linestyle = 'None')
plt.show()
data_label =data_label*0
for i in xrange(len(X_fit)):
data_label[X_fit[i],Y_fit[i],Z_new[i]] = 1
# Create output text file
sct.printv('\nWrite text file...', verbose)
file_name_label = file_data_label+'_centerline'+'.txt'
file_results = open(path_tmp + '/' + file_name_label, 'w')
min_z_index, max_z_index = min(Z), max(Z)
for i in range(min_z_index, max_z_index+1):
file_results.write(str(int(i)) + ' ' + str(X_fit[i-min_z_index]) + ' ' + str(Y_fit[i-min_z_index]) + '\n')
file_results.close()
# copy files into tmp folder
#sct.run('cp '+file_name_label+' '+path_tmp)
del data_label
##From segmentation file create centerline text file
print '\nPROCESS PART 2: From segmentation file create centerline image.'
# Extract path, file and extension
segmentation_file = os.path.abspath(segmentation_file)
path_data_seg, file_data_seg, ext_data_seg = sct.extract_fname(segmentation_file)
# copy files into tmp folder
sct.run('cp '+segmentation_file+' '+path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input segmentation into RPI
print '\nOrient segmentation image to RPI orientation...'
fname_segmentation_orient = 'tmp.segmentation_rpi' + ext_data_seg
set_orientation(file_data_seg+ext_data_seg, 'RPI', fname_segmentation_orient)
# Extract orientation of the input segmentation
orientation = get_orientation(file_data_seg+ext_data_seg)
print '\nOrientation of segmentation image: ' + orientation
# Get size of data
print '\nGet dimensions data...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_segmentation_orient)
print '.. '+str(nx)+' x '+str(ny)+' y '+str(nz)+' z '+str(nt)
print '\nOpen segmentation volume...'
file_seg = nibabel.load(fname_segmentation_orient)
data_seg = file_seg.get_data()
hdr_seg = file_seg.get_header()
# Extract min and max index in Z direction
X, Y, Z = (data_seg>0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
x_centerline = [0 for i in range(0,max_z_index-min_z_index+1)]
y_centerline = [0 for i in range(0,max_z_index-min_z_index+1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index+1)]
# Extract segmentation points and average per slice
for iz in range(min_z_index, max_z_index+1):
x_seg, y_seg = (data_seg[:,:,iz]>0).nonzero()
x_centerline[iz-min_z_index] = np.mean(x_seg)
y_centerline[iz-min_z_index] = np.mean(y_seg)
for k in range(len(X)):
data_seg[X[k],Y[k],Z[k]] = 0
# Fit the centerline points with splines and return the new fitted coordinates
#done with nurbs for now
x_centerline_fit, y_centerline_fit,x_centerline_deriv,y_centerline_deriv,z_centerline_deriv = b_spline_centerline(x_centerline,y_centerline,z_centerline)
# Create output text file
file_name_seg = file_data_seg+'_centerline'+'.txt'
sct.printv('\nWrite text file...', verbose)
file_results = open(file_name_seg, 'w')
for i in range(min_z_index, max_z_index+1):
file_results.write(str(int(i)) + ' ' + str(x_centerline_fit[i-min_z_index]) + ' ' + str(y_centerline_fit[i-min_z_index]) + '\n')
file_results.close()
del data_seg
print '\nRemoving overlap of the centerline obtain with label file if there are any:'
## Remove overlap from centerline file obtain with label file
remove_overlap(file_name_label, file_name_seg, "generated_centerline_without_overlap1.txt", parameter=1)
## Concatenation of the two centerline files
print '\nConcatenation of the two centerline files:'
if output_file_name != None :
file_name = output_file_name
else: file_name = 'centerline_total_from_label_and_seg.txt'
f_output = open(file_name, "w")
f_output.close()
with open(file_name_seg, "r") as f_seg:
with open("generated_centerline_without_overlap1.txt", "r") as f:
with open(file_name, "w") as f_output:
data_line_seg = f_seg.readlines()
data_line = f.readlines()
for line in data_line_seg :
f_output.write(line)
for line in data_line :
f_output.write(line)
# Copy result into parent folder
sct.run('cp '+file_name+' ../')
# Come back to parent folder
os.chdir('..')
# Remove temporary centerline files
if remove_temp_files:
print('\nRemove temporary files...')
sct.run('rm -rf '+path_tmp)
def main(list_file, param, output_file_name=None, remove_temp_files = 1, verbose = 0):
path, file, ext = sct.extract_fname(list_file[0])
# create temporary folder
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")
sct.run('mkdir '+path_tmp)
# copy files into tmp folder
sct.printv('\nCopy files into tmp folder...', verbose)
for i in range(len(list_file)):
file_temp = os.path.abspath(list_file[i])
sct.run('cp '+file_temp+' '+path_tmp)
# go to tmp folder
os.chdir(path_tmp)
## Concatenation of the files
# Concatenation : sum of matrices
file_0 = load(file+ext)
data_concatenation = file_0.get_data()
hdr_0 = file_0.get_header()
orientation_file_0 = get_orientation(list_file[0])
if len(list_file)>0:
for i in range(1, len(list_file)):
orientation_file_temp = get_orientation(list_file[i])
if orientation_file_0 != orientation_file_temp :
print "ERROR: The files ", list_file[0], " and ", list_file[i], " are not in the same orientation. Use sct_orientation to change the orientation of a file."
sys.exit(2)
file_temp = load(list_file[i])
data_temp = file_temp.get_data()
data_concatenation = data_concatenation + data_temp
# Save concatenation as a file
print '\nWrite NIFTI volumes...'
img = Nifti1Image(data_concatenation, None, hdr_0)
save(img,'concatenation_file.nii.gz')
# Applying nurbs to the concatenation and save file as binary file
fname_output = extract_centerline('concatenation_file.nii.gz', remove_temp_files = remove_temp_files, verbose = verbose, algo_fitting=param.algo_fitting, type_window=param.type_window, window_length=param.window_length)
# Rename files after processing
if output_file_name != None:
output_file_name = output_file_name
else : output_file_name = "generated_centerline.nii.gz"
os.rename(fname_output, output_file_name)
path_binary, file_binary, ext_binary = sct.extract_fname(output_file_name)
os.rename('concatenation_file_centerline.txt', file_binary+'.txt')
# Process for a binary file as output:
sct.run('cp '+output_file_name+' ../')
# Process for a text file as output:
sct.run('cp '+file_binary+ '.txt'+ ' ../')
os.chdir('../')
# Remove temporary files
if remove_temp_files:
print('\nRemove temporary files...')
sct.run('rm -rf '+path_tmp)
zmax_anatomic = line.split(',')[1]
zmin_seg = line.split(',')[2]
zmax_seg = line.split(',')[3]
if len(line_list) == 6:
ymin_anatomic = line.split(',')[4]
ymax_anatomic = line.split(',')[5]
file_results.close()
os.chdir(PATH_OUTPUT + '/subjects/' + subject + '/' + 'T2')
# Convert to RPI
# Input:
# - data.nii.gz
# - data_RPI.nii.gz
print '\nConvert to RPI'
orientation = get_orientation('data.nii.gz')
sct.run('sct_orientation -i data.nii.gz -s RPI')
# Crop image
if ymin_anatomic == None and ymax_anatomic == None:
sct.run(
'sct_crop_image -i data_RPI.nii.gz -o data_RPI_crop.nii.gz -dim 2 -start '
+ zmin_anatomic + ' -end ' + zmax_anatomic)
else:
sct.run(
'sct_crop_image -i data_RPI.nii.gz -o data_RPI_crop.nii.gz -dim 1,2 -start '
+ ymin_anatomic + ',' + zmin_anatomic + ' -end ' +
ymax_anatomic + ',' + zmax_anatomic)
# sct.run('sct_orientation -i data_RPI_crop.nii.gz -o data_crop.nii.gz -s '+ orientation)
# denoising
# input:
def check_if_rpi(fname):
from sct_orientation import get_orientation
if not get_orientation(fname) == 'RPI':
printv('\nERROR: '+fname+' is not in RPI orientation. Use sct_orientation to reorient your data. Exit program.\n', 1, 'error')
def extract_centerline(fname_segmentation, remove_temp_files, name_output='', verbose = 0, algo_fitting = 'hanning', type_window = 'hanning', window_length = 80):
# Extract path, file and extension
fname_segmentation = os.path.abspath(fname_segmentation)
path_data, file_data, ext_data = sct.extract_fname(fname_segmentation)
# create temporary folder
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")
sct.run('mkdir '+path_tmp)
# copy files into tmp folder
sct.run('cp '+fname_segmentation+' '+path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input centerline into RPI
sct.printv('\nOrient centerline to RPI orientation...', verbose)
fname_segmentation_orient = 'segmentation_rpi' + ext_data
set_orientation(file_data+ext_data, 'RPI', fname_segmentation_orient)
# Get dimension
sct.printv('\nGet dimensions...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_segmentation_orient).dim
sct.printv('.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz), verbose)
sct.printv('.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm', verbose)
# Extract orientation of the input segmentation
orientation = get_orientation(file_data+ext_data)
sct.printv('\nOrientation of segmentation image: ' + orientation, verbose)
sct.printv('\nOpen segmentation volume...', verbose)
file = nibabel.load(fname_segmentation_orient)
data = file.get_data()
hdr = file.get_header()
# Extract min and max index in Z direction
X, Y, Z = (data>0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
x_centerline = [0 for i in range(0,max_z_index-min_z_index+1)]
y_centerline = [0 for i in range(0,max_z_index-min_z_index+1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index+1)]
# Extract segmentation points and average per slice
for iz in range(min_z_index, max_z_index+1):
x_seg, y_seg = (data[:,:,iz]>0).nonzero()
x_centerline[iz-min_z_index] = np.mean(x_seg)
y_centerline[iz-min_z_index] = np.mean(y_seg)
for k in range(len(X)):
data[X[k], Y[k], Z[k]] = 0
# extract centerline and smooth it
x_centerline_fit, y_centerline_fit, z_centerline_fit, x_centerline_deriv,y_centerline_deriv,z_centerline_deriv = smooth_centerline(fname_segmentation_orient, type_window = type_window, window_length = window_length, algo_fitting = algo_fitting, verbose = verbose)
if verbose == 2:
import matplotlib.pyplot as plt
#Creation of a vector x that takes into account the distance between the labels
nz_nonz = len(z_centerline)
x_display = [0 for i in range(x_centerline_fit.shape[0])]
y_display = [0 for i in range(y_centerline_fit.shape[0])]
for i in range(0, nz_nonz, 1):
x_display[int(z_centerline[i]-z_centerline[0])] = x_centerline[i]
y_display[int(z_centerline[i]-z_centerline[0])] = y_centerline[i]
plt.figure(1)
plt.subplot(2,1,1)
plt.plot(z_centerline_fit, x_display, 'ro')
plt.plot(z_centerline_fit, x_centerline_fit)
plt.xlabel("Z")
plt.ylabel("X")
plt.title("x and x_fit coordinates")
plt.subplot(2,1,2)
plt.plot(z_centerline_fit, y_display, 'ro')
plt.plot(z_centerline_fit, y_centerline_fit)
plt.xlabel("Z")
plt.ylabel("Y")
plt.title("y and y_fit coordinates")
plt.show()
# Create an image with the centerline
for iz in range(min_z_index, max_z_index+1):
data[round(x_centerline_fit[iz-min_z_index]), round(y_centerline_fit[iz-min_z_index]), iz] = 1 # if index is out of bounds here for hanning: either the segmentation has holes or labels have been added to the file
# Write the centerline image in RPI orientation
hdr.set_data_dtype('uint8') # set imagetype to uint8
sct.printv('\nWrite NIFTI volumes...', verbose)
img = nibabel.Nifti1Image(data, None, hdr)
nibabel.save(img, 'centerline.nii.gz')
# Define name if output name is not specified
if name_output=='csa_volume.nii.gz' or name_output=='':
# sct.generate_output_file('centerline.nii.gz', file_data+'_centerline'+ext_data, verbose)
name_output = file_data+'_centerline'+ext_data
sct.generate_output_file('centerline.nii.gz', name_output, verbose)
# create a txt file with the centerline
path, rad_output, ext = sct.extract_fname(name_output)
name_output_txt = rad_output + '.txt'
sct.printv('\nWrite text file...', verbose)
file_results = open(name_output_txt, 'w')
for i in range(min_z_index, max_z_index+1):
file_results.write(str(int(i)) + ' ' + str(x_centerline_fit[i-min_z_index]) + ' ' + str(y_centerline_fit[i-min_z_index]) + '\n')
file_results.close()
# Copy result into parent folder
sct.run('cp '+name_output_txt+' ../')
del data
# come back to parent folder
os.chdir('..')
# Change orientation of the output centerline into input orientation
sct.printv('\nOrient centerline image to input orientation: ' + orientation, verbose)
fname_segmentation_orient = 'tmp.segmentation_rpi' + ext_data
set_orientation(path_tmp+'/'+name_output, orientation, name_output)
# Remove temporary files
if remove_temp_files:
sct.printv('\nRemove temporary files...', verbose)
sct.run('rm -rf '+path_tmp, verbose)
return name_output
def generate_warping_field(fname_dest,
x_trans,
y_trans,
theta_rot=None,
center_rotation=None,
fname='warping_field.nii.gz',
verbose=1):
"""Generation of a warping field towards an image and given transformation parameters.
Given a destination image and transformation parameters this functions creates a NIFTI 3D warping field that can be
applied afterwards. The transformation parameters corresponds to a slice-by-slice registration of images, thus the
transformation parameters must be precised for each slice of the image.
inputs:
fname_dest: name of destination image (type: string)
x_trans: list of translations along x axis for each slice (type: list, length: height of fname_dest)
y_trans: list of translations along y axis for each slice (type: list, length: height of fname_dest)
theta_rot[optional]: list of rotation angles in radian (and in ITK's coordinate system) for each slice (type: list)
center_rotation[optional]: pixel coordinates in plan xOy of the wanted center of rotation (type: list,
length: 2, example: [0,ny/2])
fname[optional]: name of output warp (type: string)
verbose: display parameter (type: int)
output:
creation of a warping field of name 'fname' with an header similar to the destination image.
"""
from nibabel import load
from math import cos, sin
from sct_orientation import get_orientation
#Make sure image is in rpi format
sct.printv(
'\nChecking if the image of destination is in RPI orientation for the warping field generation ...',
verbose)
orientation = get_orientation(fname_dest)
if orientation != 'RPI':
sct.printv(
'\nWARNING: The image of destination is not in RPI format. Dimensions of the warping field might be inverted.',
verbose)
else:
sct.printv('\tOK', verbose)
sct.printv(
'\n\nCreating warping field ' + fname +
' for transformations along z...', verbose)
file_dest = load(fname_dest)
hdr_file_dest = file_dest.get_header()
hdr_warp = hdr_file_dest.copy()
# Get image dimensions
sct.printv('\nGet image dimensions of destination image...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_dest).dim
sct.printv(
'.. matrix size: ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz),
verbose)
sct.printv(
'.. voxel size: ' + str(px) + 'mm x ' + str(py) + 'mm x ' + str(pz) +
'mm', verbose)
#Center of rotation
if center_rotation == None:
x_a = int(round(nx / 2))
y_a = int(round(ny / 2))
else:
x_a = center_rotation[0]
y_a = center_rotation[1]
# Calculate displacement for each voxel
data_warp = zeros(((((nx, ny, nz, 1, 3)))))
# For translations
if theta_rot == None:
for i in range(nx):
for j in range(ny):
for k in range(nz):
data_warp[i, j, k, 0, 0] = x_trans[k]
data_warp[i, j, k, 0, 1] = y_trans[k]
data_warp[i, j, k, 0, 2] = 0
# # For rigid transforms (not optimized)
# if theta_rot != None:
# for k in range(nz):
# for i in range(nx):
# for j in range(ny):
# # data_warp[i, j, k, 0, 0] = (cos(theta_rot[k])-1) * (i - x_a) - sin(theta_rot[k]) * (j - y_a) - x_trans[k]
# # data_warp[i, j, k, 0, 1] = -(sin(theta_rot[k]) * (i - x_a) + (cos(theta_rot[k])-1) * (j - y_a)) + y_trans[k]
#
# data_warp[i, j, k, 0, 0] = (cos(theta_rot[k]) - 1) * (i - x_a) - sin(theta_rot[k]) * (j - y_a) + x_trans[k] #+ sin(theta_rot[k]) * (int(round(nx/2))-x_a)
# data_warp[i, j, k, 0, 1] = - sin(theta_rot[k]) * (i - x_a) - (cos(theta_rot[k]) - 1) * (j - y_a) + y_trans[k] #- sin(theta_rot[k]) * (int(round(nx/2))-x_a)
# data_warp[i, j, k, 0, 2] = 0
# For rigid transforms with array (time optimization)
if theta_rot != None:
vector_i = [[[i - x_a], [j - y_a]] for i in range(nx)
for j in range(ny)]
for k in range(nz):
matrix_rot_a = asarray([[cos(theta_rot[k]), -sin(theta_rot[k])],
[-sin(theta_rot[k]), -cos(theta_rot[k])]])
tmp = matrix_rot_a + array(((-1, 0), (0, 1)))
result = dot(tmp,
array(vector_i).T[0]) + array([[x_trans[k]],
[y_trans[k]]])
for i in range(nx):
data_warp[i, :, k, 0, 0] = result[0][i * nx:i * nx + ny]
data_warp[i, :, k, 0, 1] = result[1][i * nx:i * nx + ny]
# Generate warp file as a warping field
hdr_warp.set_intent('vector', (), '')
hdr_warp.set_data_dtype('float32')
img = nibabel.Nifti1Image(data_warp, None, hdr_warp)
nibabel.save(img, fname)
sct.printv('\nDONE ! Warping field generated: ' + fname, verbose)
def generate_warping_field(fname_dest, x_trans, y_trans, theta_rot=None, center_rotation=None, fname='warping_field.nii.gz', verbose=1):
"""Generation of a warping field towards an image and given transformation parameters.
Given a destination image and transformation parameters this functions creates a NIFTI 3D warping field that can be
applied afterwards. The transformation parameters corresponds to a slice-by-slice registration of images, thus the
transformation parameters must be precised for each slice of the image.
inputs:
fname_dest: name of destination image (type: string)
x_trans: list of translations along x axis for each slice (type: list, length: height of fname_dest)
y_trans: list of translations along y axis for each slice (type: list, length: height of fname_dest)
theta_rot[optional]: list of rotation angles in radian (and in ITK's coordinate system) for each slice (type: list)
center_rotation[optional]: pixel coordinates in plan xOy of the wanted center of rotation (type: list,
length: 2, example: [0,ny/2])
fname[optional]: name of output warp (type: string)
verbose: display parameter (type: int)
output:
creation of a warping field of name 'fname' with an header similar to the destination image.
"""
from nibabel import load
from math import cos, sin
from sct_orientation import get_orientation
#Make sure image is in rpi format
sct.printv('\nChecking if the image of destination is in RPI orientation for the warping field generation ...', verbose)
orientation = get_orientation(fname_dest)
if orientation != 'RPI':
sct.printv('\nWARNING: The image of destination is not in RPI format. Dimensions of the warping field might be inverted.', verbose)
else: sct.printv('\tOK', verbose)
sct.printv('\n\nCreating warping field ' + fname + ' for transformations along z...', verbose)
file_dest = load(fname_dest)
hdr_file_dest = file_dest.get_header()
hdr_warp = hdr_file_dest.copy()
# Get image dimensions
sct.printv('\nGet image dimensions of destination image...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_dest).dim
sct.printv('.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz), verbose)
sct.printv('.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm', verbose)
#Center of rotation
if center_rotation == None:
x_a = int(round(nx/2))
y_a = int(round(ny/2))
else:
x_a = center_rotation[0]
y_a = center_rotation[1]
# Calculate displacement for each voxel
data_warp = zeros(((((nx, ny, nz, 1, 3)))))
# For translations
if theta_rot == None:
for i in range(nx):
for j in range(ny):
for k in range(nz):
data_warp[i, j, k, 0, 0] = x_trans[k]
data_warp[i, j, k, 0, 1] = y_trans[k]
data_warp[i, j, k, 0, 2] = 0
# # For rigid transforms (not optimized)
# if theta_rot != None:
# for k in range(nz):
# for i in range(nx):
# for j in range(ny):
# # data_warp[i, j, k, 0, 0] = (cos(theta_rot[k])-1) * (i - x_a) - sin(theta_rot[k]) * (j - y_a) - x_trans[k]
# # data_warp[i, j, k, 0, 1] = -(sin(theta_rot[k]) * (i - x_a) + (cos(theta_rot[k])-1) * (j - y_a)) + y_trans[k]
#
# data_warp[i, j, k, 0, 0] = (cos(theta_rot[k]) - 1) * (i - x_a) - sin(theta_rot[k]) * (j - y_a) + x_trans[k] #+ sin(theta_rot[k]) * (int(round(nx/2))-x_a)
# data_warp[i, j, k, 0, 1] = - sin(theta_rot[k]) * (i - x_a) - (cos(theta_rot[k]) - 1) * (j - y_a) + y_trans[k] #- sin(theta_rot[k]) * (int(round(nx/2))-x_a)
# data_warp[i, j, k, 0, 2] = 0
# For rigid transforms with array (time optimization)
if theta_rot != None:
vector_i = [[[i-x_a],[j-y_a]] for i in range(nx) for j in range(ny)]
for k in range(nz):
matrix_rot_a = asarray([[cos(theta_rot[k]), - sin(theta_rot[k])],[-sin(theta_rot[k]), -cos(theta_rot[k])]])
tmp = matrix_rot_a + array(((-1,0),(0,1)))
result = dot(tmp, array(vector_i).T[0]) + array([[x_trans[k]], [y_trans[k]]])
for i in range(nx):
data_warp[i, :, k, 0, 0] = result[0][i*nx:i*nx+ny]
data_warp[i, :, k, 0, 1] = result[1][i*nx:i*nx+ny]
# Generate warp file as a warping field
hdr_warp.set_intent('vector', (), '')
hdr_warp.set_data_dtype('float32')
img = nibabel.Nifti1Image(data_warp, None, hdr_warp)
nibabel.save(img, fname)
sct.printv('\nDONE ! Warping field generated: '+fname, verbose)
def main():
# Initialization to defaults parameters
fname_data = '' # data is empty by default
path_label = '' # empty by default
method = param.method # extraction mode by default
labels_of_interest = param.labels_of_interest
slices_of_interest = param.slices_of_interest
vertebral_levels = param.vertebral_levels
average_all_labels = param.average_all_labels
fname_output = param.fname_output
fname_vertebral_labeling = param.fname_vertebral_labeling
fname_normalizing_label = '' # optional then default is empty
normalization_method = '' # optional then default is empty
actual_vert_levels = None # variable used in case the vertebral levels asked by the user don't correspond exactly to the vertebral levels available in the metric data
warning_vert_levels = None # variable used to warn the user in case the vertebral levels he asked don't correspond exactly to the vertebral levels available in the metric data
verbose = param.verbose
flag_h = 0
ml_clusters = param.ml_clusters
adv_param = param.adv_param
adv_param_user = ''
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
status, path_sct_data = commands.getstatusoutput('echo $SCT_TESTING_DATA_DIR')
fname_data = '/Users/julien/data/temp/sct_example_data/mt/mtr.nii.gz'
path_label = '/Users/julien/data/temp/sct_example_data/mt/label/atlas/'
method = 'map'
ml_clusters = '0:29,30,31'
labels_of_interest = '0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29'
slices_of_interest = ''
vertebral_levels = ''
average_all_labels = 1
fname_normalizing_label = '' #path_sct+'/testing/data/errsm_23/mt/label/template/MNI-Poly-AMU_CSF.nii.gz'
normalization_method = '' #'whole'
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'haf:i:l:m:n:o:p:v:w:z:') # define flags
except getopt.GetoptError as err: # check if the arguments are defined
print str(err) # error
usage() # display usage
if not opts:
usage()
for opt, arg in opts: # explore flags
if opt in '-a':
average_all_labels = 1
elif opt in '-f':
path_label = os.path.abspath(arg) # save path of labels folder
elif opt == '-h': # help option
flag_h = 1
elif opt in '-i':
fname_data = arg
elif opt in '-l':
labels_of_interest = arg
elif opt in '-m': # method for metric extraction
method = arg
elif opt in '-n': # filename of the label by which the user wants to normalize
fname_normalizing_label = arg
elif opt in '-o': # output option
fname_output = arg # fname of output file
elif opt in '-p':
adv_param_user = arg
elif opt in '-v':
# vertebral levels option, if the user wants to average the metric across specific vertebral levels
vertebral_levels = arg
elif opt in '-w':
# method used for the normalization by the metric estimation into the normalizing label (see flag -n): 'sbs' for slice-by-slice or 'whole' for normalization after estimation in the whole labels
normalization_method = arg
elif opt in '-z': # slices numbers option
slices_of_interest = arg # save labels numbers
# Display usage with tract parameters by default in case files aren't chosen in arguments inputs
if fname_data == '' or path_label == '' or flag_h:
param.path_label = path_label
usage()
# Check existence of data file
sct.printv('\ncheck existence of input files...', verbose)
sct.check_file_exist(fname_data)
sct.check_folder_exist(path_label)
if fname_normalizing_label:
sct.check_folder_exist(fname_normalizing_label)
# add slash at the end
path_label = sct.slash_at_the_end(path_label, 1)
# Find path to the vertebral labeling file if vertebral levels were specified by the user
if vertebral_levels:
if slices_of_interest: # impossible to select BOTH specific slices and specific vertebral levels
print '\nERROR: You cannot select BOTH vertebral levels AND slice numbers.'
usage()
else:
fname_vertebral_labeling_list = sct.find_file_within_folder(fname_vertebral_labeling, path_label + '..')
if len(fname_vertebral_labeling_list) > 1:
print color.red + 'ERROR: More than one file named \'' + fname_vertebral_labeling + ' were found in ' + path_label + '. Exit program.' + color.end
sys.exit(2)
elif len(fname_vertebral_labeling_list) == 0:
print color.red + 'ERROR: No file named \'' + fname_vertebral_labeling + ' were found in ' + path_label + '. Exit program.' + color.end
sys.exit(2)
else:
fname_vertebral_labeling = os.path.abspath(fname_vertebral_labeling_list[0])
# Check input parameters
check_method(method, fname_normalizing_label, normalization_method)
# parse argument for param
if not adv_param_user == '':
adv_param = adv_param_user.replace(' ', '').split(',') # remove spaces and parse with comma
del adv_param_user # clean variable
# TODO: check integrity of input
# Extract label info
label_id, label_name, label_file = read_label_file(path_label, param.file_info_label)
nb_labels_total = len(label_id)
# check consistency of label input parameter.
label_id_user, average_all_labels = check_labels(labels_of_interest, nb_labels_total, average_all_labels, method) # If 'labels_of_interest' is empty, then
# 'label_id_user' contains the index of all labels in the file info_label.txt
# print parameters
print '\nChecked parameters:'
print ' data ...................... '+fname_data
print ' folder label .............. '+path_label
print ' selected labels ........... '+str(label_id_user)
print ' estimation method ......... '+method
print ' slices of interest ........ '+slices_of_interest
print ' vertebral levels .......... '+vertebral_levels
print ' vertebral labeling file.... '+fname_vertebral_labeling
print ' advanced parameters ....... '+str(adv_param)
# Check if the orientation of the data is RPI
orientation_data = get_orientation(fname_data)
# If orientation is not RPI, change to RPI
if orientation_data != 'RPI':
sct.printv('\nCreate temporary folder to change the orientation of the NIFTI files into RPI...', verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.create_folder(path_tmp)
# change orientation and load data
sct.printv('\nChange image orientation and load it...', verbose)
data = nib.load(set_orientation(fname_data, 'RPI', path_tmp+'orient_data.nii')).get_data()
# Do the same for labels
sct.printv('\nChange labels orientation and load them...', verbose)
labels = np.empty([nb_labels_total], dtype=object) # labels(nb_labels_total, x, y, z)
for i_label in range(0, nb_labels_total):
labels[i_label] = nib.load(set_orientation(path_label+label_file[i_label], 'RPI', path_tmp+'orient_'+label_file[i_label])).get_data()
if fname_normalizing_label: # if the "normalization" option is wanted,
normalizing_label = np.empty([1], dtype=object) # choose this kind of structure so as to keep easily the
# compatibility with the rest of the code (dimensions: (1, x, y, z))
normalizing_label[0] = nib.load(set_orientation(fname_normalizing_label, 'RPI', path_tmp+'orient_normalizing_volume.nii')).get_data()
if vertebral_levels: # if vertebral levels were selected,
data_vertebral_labeling = nib.load(set_orientation(fname_vertebral_labeling, 'RPI', path_tmp+'orient_vertebral_labeling.nii.gz')).get_data()
# Remove the temporary folder used to change the NIFTI files orientation into RPI
sct.printv('\nRemove the temporary folder...', verbose)
status, output = commands.getstatusoutput('rm -rf ' + path_tmp)
else:
# Load image
sct.printv('\nLoad image...', verbose)
data = nib.load(fname_data).get_data()
# Load labels
sct.printv('\nLoad labels...', verbose)
labels = np.empty([nb_labels_total], dtype=object) # labels(nb_labels_total, x, y, z)
for i_label in range(0, nb_labels_total):
labels[i_label] = nib.load(path_label+label_file[i_label]).get_data()
if fname_normalizing_label: # if the "normalization" option is wanted,
normalizing_label = np.empty([1], dtype=object) # choose this kind of structure so as to keep easily the
# compatibility with the rest of the code (dimensions: (1, x, y, z))
normalizing_label[0] = nib.load(fname_normalizing_label).get_data() # load the data of the normalizing label
if vertebral_levels: # if vertebral levels were selected,
data_vertebral_labeling = nib.load(fname_vertebral_labeling).get_data()
# Change metric data type into floats for future manipulations (normalization)
data = np.float64(data)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz = data.shape
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# Get dimensions of labels
sct.printv('\nGet dimensions of label...', verbose)
nx_atlas, ny_atlas, nz_atlas = labels[0].shape
sct.printv('.. '+str(nx_atlas)+' x '+str(ny_atlas)+' x '+str(nz_atlas)+' x '+str(nb_labels_total), verbose)
# Check dimensions consistency between atlas and data
if (nx, ny, nz) != (nx_atlas, ny_atlas, nz_atlas):
print '\nERROR: Metric data and labels DO NOT HAVE SAME DIMENSIONS.'
sys.exit(2)
# Update the flag "slices_of_interest" according to the vertebral levels selected by user (if it's the case)
if vertebral_levels:
slices_of_interest, actual_vert_levels, warning_vert_levels = \
get_slices_matching_with_vertebral_levels(data, vertebral_levels, data_vertebral_labeling)
# select slice of interest by cropping data and labels
if slices_of_interest:
data = remove_slices(data, slices_of_interest)
for i_label in range(0, nb_labels_total):
labels[i_label] = remove_slices(labels[i_label], slices_of_interest)
if fname_normalizing_label: # if the "normalization" option was selected,
normalizing_label[0] = remove_slices(normalizing_label[0], slices_of_interest)
# if user wants to get unique value across labels, then combine all labels together
if average_all_labels == 1:
sum_labels_user = np.sum(labels[label_id_user]) # sum the labels selected by user
if method == 'ml' or method == 'map': # in case the maximum likelihood and the average across different labels are wanted
labels_tmp = np.empty([nb_labels_total - len(label_id_user) + 1], dtype=object)
labels = np.delete(labels, label_id_user) # remove the labels selected by user
labels_tmp[0] = sum_labels_user # put the sum of the labels selected by user in first position of the tmp
# variable
for i_label in range(1, len(labels_tmp)):
labels_tmp[i_label] = labels[i_label-1] # fill the temporary array with the values of the non-selected labels
labels = labels_tmp # replace the initial labels value by the updated ones (with the summed labels)
del labels_tmp # delete the temporary labels
else: # in other cases than the maximum likelihood, we can remove other labels (not needed for estimation)
labels = np.empty(1, dtype=object)
labels[0] = sum_labels_user # we create a new label array that includes only the summed labels
if fname_normalizing_label: # if the "normalization" option is wanted
sct.printv('\nExtract normalization values...', verbose)
if normalization_method == 'sbs': # case: the user wants to normalize slice-by-slice
for z in range(0, data.shape[-1]):
normalizing_label_slice = np.empty([1], dtype=object) # in order to keep compatibility with the function
# 'extract_metric_within_tract', define a new array for the slice z of the normalizing labels
normalizing_label_slice[0] = normalizing_label[0][..., z]
metric_normalizing_label = extract_metric_within_tract(data[..., z], normalizing_label_slice, method, 0)
# estimate the metric mean in the normalizing label for the slice z
if metric_normalizing_label[0][0] != 0:
data[..., z] = data[..., z]/metric_normalizing_label[0][0] # divide all the slice z by this value
elif normalization_method == 'whole': # case: the user wants to normalize after estimations in the whole labels
metric_mean_norm_label, metric_std_norm_label = extract_metric_within_tract(data, normalizing_label, method, param.verbose) # mean and std are lists
# identify cluster for each tract (for use with robust ML)
ml_clusters_array = get_clusters(ml_clusters, labels)
# extract metrics within labels
sct.printv('\nExtract metric within labels...', verbose)
metric_mean, metric_std = extract_metric_within_tract(data, labels, method, verbose, ml_clusters_array, adv_param) # mean and std are lists
if fname_normalizing_label and normalization_method == 'whole': # case: user wants to normalize after estimations in the whole labels
metric_mean, metric_std = np.divide(metric_mean, metric_mean_norm_label), np.divide(metric_std, metric_std_norm_label)
# update label name if average
if average_all_labels == 1:
label_name[0] = 'AVERAGED'+' -'.join(label_name[i] for i in label_id_user) # concatenate the names of the
# labels selected by the user if the average tag was asked
label_id_user = [0] # update "label_id_user" to select the "averaged" label (which is in first position)
metric_mean = metric_mean[label_id_user]
metric_std = metric_std[label_id_user]
# display metrics
sct.printv('\nEstimation results:', 1)
for i in range(0, metric_mean.size):
sct.printv(str(label_id_user[i])+', '+str(label_name[label_id_user[i]])+': '+str(metric_mean[i])+' +/- '+str(metric_std[i]), 1, 'info')
# save and display metrics
save_metrics(label_id_user, label_name, slices_of_interest, metric_mean, metric_std, fname_output, fname_data,
method, fname_normalizing_label, actual_vert_levels, warning_vert_levels)
def change_orientation(self, orientation='RPI', inversion_orient=False):
"""
This function changes the orientation of the data by swapping the image axis.
Warning: the nifti image header is not changed!!!
:param orientation: string of three character representing the new orientation (ex: AIL, default: RPI)
inversion_orient: boolean. If True, the data change to match the orientation in the header, based on the orientation provided as the argument orientation.
:return:
"""
opposite_character = {
'L': 'R',
'R': 'L',
'A': 'P',
'P': 'A',
'I': 'S',
'S': 'I'
}
if self.orientation is None:
from sct_orientation import get_orientation
self.orientation = get_orientation(self.file_name)
if inversion_orient:
temp_orientation = self.orientation
self.orientation = orientation
orientation = temp_orientation
# change the orientation of the image
perm = [0, 1, 2]
inversion = [1, 1, 1]
for i, character in enumerate(self.orientation):
try:
perm[i] = orientation.index(character)
except ValueError:
perm[i] = orientation.index(opposite_character[character])
inversion[i] = -1
# axes inversion
self.data = self.data[::inversion[0], ::inversion[1], ::inversion[2]]
# axes manipulations
from numpy import swapaxes
if perm == [1, 0, 2]:
self.data = swapaxes(self.data, 0, 1)
elif perm == [2, 1, 0]:
self.data = swapaxes(self.data, 0, 2)
elif perm == [0, 2, 1]:
self.data = swapaxes(self.data, 1, 2)
elif perm == [2, 1, 0]:
self.data = swapaxes(self.data, 0, 2)
elif perm == [2, 0, 1]:
self.data = swapaxes(self.data, 0,
2) # transform [2, 0, 1] to [1, 0, 2]
self.data = swapaxes(self.data, 0,
1) # transform [1, 0, 2] to [0, 1, 2]
elif perm == [1, 2, 0]:
self.data = swapaxes(self.data, 0,
2) # transform [1, 2, 0] to [0, 2, 1]
self.data = swapaxes(self.data, 1,
2) # transform [0, 2, 1] to [0, 1, 2]
elif perm == [0, 1, 2]:
# do nothing
pass
else:
print 'Error: wrong orientation'
self.orientation = orientation
zmax_anatomic = line.split(',')[1]
zmin_seg = line.split(',')[2]
zmax_seg = line.split(',')[3]
if len(line_list)==6:
ymin_anatomic = line.split(',')[4]
ymax_anatomic = line.split(',')[5]
file_results.close()
os.chdir(PATH_OUTPUT + '/subjects/'+subject+'/'+'T2')
# Convert to RPI
# Input:
# - data.nii.gz
# - data_RPI.nii.gz
print '\nConvert to RPI'
orientation = get_orientation('data.nii.gz')
sct.run('sct_orientation -i data.nii.gz -s RPI')
# Crop image
if ymin_anatomic == None and ymax_anatomic == None:
sct.run('sct_crop_image -i data_RPI.nii.gz -o data_RPI_crop.nii.gz -dim 2 -start ' + zmin_anatomic + ' -end ' + zmax_anatomic )
else: sct.run('sct_crop_image -i data_RPI.nii.gz -o data_RPI_crop.nii.gz -dim 1,2 -start ' + ymin_anatomic +','+zmin_anatomic+ ' -end ' + ymax_anatomic+','+zmax_anatomic )
# sct.run('sct_orientation -i data_RPI_crop.nii.gz -o data_crop.nii.gz -s '+ orientation)
# denoising
# input:
# - data_crop.nii.gz
# output:
# - data_crop_denoised.nii.gz
#print '\nDenoising image data_RPI_crop.nii.gz...'
#sct.printv('sct_denoising_onlm.py -i data_RPI_crop.nii.gz')
#sct.run('sct_denoising_onlm.py -i data_RPI_crop.nii.gz')
def main():
# Initialization
fname_anat = ''
fname_centerline = ''
centerline_fitting = 'polynome'
remove_temp_files = param.remove_temp_files
interp = param.interp
degree_poly = param.deg_poly
# extract path of the script
path_script = os.path.dirname(__file__)+'/'
# Parameters for debug mode
if param.debug == 1:
print '\n*** WARNING: DEBUG MODE ON ***\n'
status, path_sct_data = commands.getstatusoutput('echo $SCT_TESTING_DATA_DIR')
fname_anat = path_sct_data+'/t2/t2.nii.gz'
fname_centerline = path_sct_data+'/t2/t2_seg.nii.gz'
else:
# Check input param
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:c:r:d:f:s:')
except getopt.GetoptError as err:
print str(err)
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_anat = arg
elif opt in ('-c'):
fname_centerline = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ('-d'):
degree_poly = int(arg)
elif opt in ('-f'):
centerline_fitting = str(arg)
elif opt in ('-s'):
interp = str(arg)
# display usage if a mandatory argument is not provided
if fname_anat == '' or fname_centerline == '':
usage()
# check existence of input files
sct.check_file_exist(fname_anat)
sct.check_file_exist(fname_centerline)
# extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
# Display arguments
print '\nCheck input arguments...'
print ' Input volume ...................... '+fname_anat
print ' Centerline ........................ '+fname_centerline
print ''
# Get input image orientation
input_image_orientation = get_orientation(fname_anat)
# Reorient input data into RL PA IS orientation
set_orientation(fname_anat, 'RPI', 'tmp.anat_orient.nii')
set_orientation(fname_centerline, 'RPI', 'tmp.centerline_orient.nii')
# Open centerline
#==========================================================================================
print '\nGet dimensions of input centerline...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension('tmp.centerline_orient.nii')
print '.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz)
print '.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm'
print '\nOpen centerline volume...'
file = nibabel.load('tmp.centerline_orient.nii')
data = file.get_data()
X, Y, Z = (data>0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
# loop across z and associate x,y coordinate with the point having maximum intensity
x_centerline = [0 for iz in range(min_z_index, max_z_index+1, 1)]
y_centerline = [0 for iz in range(min_z_index, max_z_index+1, 1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index+1, 1)]
# Two possible scenario:
# 1. the centerline is probabilistic: each slices contains voxels with the probability of containing the centerline [0:...:1]
# We only take the maximum value of the image to aproximate the centerline.
# 2. The centerline/segmentation image contains many pixels per slice with values {0,1}.
# We take all the points and approximate the centerline on all these points.
X, Y, Z = ((data<1)*(data>0)).nonzero() # X is empty if binary image
if (len(X) > 0): # Scenario 1
for iz in range(min_z_index, max_z_index+1, 1):
x_centerline[iz-min_z_index], y_centerline[iz-min_z_index] = numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape)
else: # Scenario 2
for iz in range(min_z_index, max_z_index+1, 1):
x_seg, y_seg = (data[:,:,iz]>0).nonzero()
if len(x_seg) > 0:
x_centerline[iz-min_z_index] = numpy.mean(x_seg)
y_centerline[iz-min_z_index] = numpy.mean(y_seg)
# TODO: find a way to do the previous loop with this, which is more neat:
# [numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape) for iz in range(0,nz,1)]
# clear variable
del data
# Fit the centerline points with the kind of curve given as argument of the script and return the new smoothed coordinates
if centerline_fitting == 'splines':
try:
x_centerline_fit, y_centerline_fit = b_spline_centerline(x_centerline,y_centerline,z_centerline)
except ValueError:
print "splines fitting doesn't work, trying with polynomial fitting...\n"
x_centerline_fit, y_centerline_fit = polynome_centerline(x_centerline,y_centerline,z_centerline)
elif centerline_fitting == 'polynome':
x_centerline_fit, y_centerline_fit = polynome_centerline(x_centerline,y_centerline,z_centerline)
#==========================================================================================
# Split input volume
print '\nSplit input volume...'
sct.run(sct.fsloutput + 'fslsplit tmp.anat_orient.nii tmp.anat_z -z')
file_anat_split = ['tmp.anat_z'+str(z).zfill(4) for z in range(0,nz,1)]
# initialize variables
file_mat_inv_cumul = ['tmp.mat_inv_cumul_z'+str(z).zfill(4) for z in range(0,nz,1)]
z_init = min_z_index
displacement_max_z_index = x_centerline_fit[z_init-min_z_index]-x_centerline_fit[max_z_index-min_z_index]
# write centerline as text file
print '\nGenerate fitted transformation matrices...'
file_mat_inv_cumul_fit = ['tmp.mat_inv_cumul_fit_z'+str(z).zfill(4) for z in range(0,nz,1)]
for iz in range(min_z_index, max_z_index+1, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], 'w')
if (x_centerline[iz-min_z_index] == 0 and y_centerline[iz-min_z_index] == 0):
displacement = 0
else:
displacement = x_centerline_fit[z_init-min_z_index]-x_centerline_fit[iz-min_z_index]
fid.write('%i %i %i %f\n' %(1, 0, 0, displacement) )
fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
# we complete the displacement matrix in z direction
for iz in range(0, min_z_index, 1):
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' %(1, 0, 0, 0) )
fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
for iz in range(max_z_index+1, nz, 1):
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' %(1, 0, 0, displacement_max_z_index) )
fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
# apply transformations to data
print '\nApply fitted transformation matrices...'
file_anat_split_fit = ['tmp.anat_orient_fit_z'+str(z).zfill(4) for z in range(0,nz,1)]
for iz in range(0, nz, 1):
# forward cumulative transformation to data
sct.run(fsloutput+'flirt -in '+file_anat_split[iz]+' -ref '+file_anat_split[iz]+' -applyxfm -init '+file_mat_inv_cumul_fit[iz]+' -out '+file_anat_split_fit[iz]+' -interp '+interp)
# Merge into 4D volume
print '\nMerge into 4D volume...'
sct.run(fsloutput+'fslmerge -z tmp.anat_orient_fit tmp.anat_orient_fit_z*')
# Reorient data as it was before
print '\nReorient data back into native orientation...'
set_orientation('tmp.anat_orient_fit.nii', input_image_orientation, 'tmp.anat_orient_fit_reorient.nii')
# Generate output file (in current folder)
print '\nGenerate output file (in current folder)...'
sct.generate_output_file('tmp.anat_orient_fit_reorient.nii', file_anat+'_flatten'+ext_anat)
# Delete temporary files
if remove_temp_files == 1:
print '\nDelete temporary files...'
sct.run('rm -rf tmp.*')
# to view results
print '\nDone! To view results, type:'
print 'fslview '+file_anat+ext_anat+' '+file_anat+'_flatten'+ext_anat+' &\n'
def main():
# Initialization to defaults parameters
fname_data = '' # data is empty by default
path_label = '' # empty by default
method = param.method # extraction mode by default
labels_of_interest = param.labels_of_interest
slices_of_interest = param.slices_of_interest
vertebral_levels = param.vertebral_levels
average_all_labels = param.average_all_labels
fname_output = param.fname_output
fname_vertebral_labeling = param.fname_vertebral_labeling
fname_normalizing_label = '' # optional then default is empty
normalization_method = '' # optional then default is empty
actual_vert_levels = None # variable used in case the vertebral levels asked by the user don't correspond exactly to the vertebral levels available in the metric data
warning_vert_levels = None # variable used to warn the user in case the vertebral levels he asked don't correspond exactly to the vertebral levels available in the metric data
verbose = param.verbose
flag_h = 0
ml_clusters = param.ml_clusters
adv_param = param.adv_param
adv_param_user = ''
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
status, path_sct_data = commands.getstatusoutput(
'echo $SCT_TESTING_DATA_DIR')
fname_data = '/Users/julien/data/temp/sct_example_data/mt/mtr.nii.gz'
path_label = '/Users/julien/data/temp/sct_example_data/mt/label/atlas/'
method = 'map'
ml_clusters = '0:29,30,31'
labels_of_interest = '0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29'
slices_of_interest = ''
vertebral_levels = ''
average_all_labels = 1
fname_normalizing_label = '' #path_sct+'/testing/data/errsm_23/mt/label/template/MNI-Poly-AMU_CSF.nii.gz'
normalization_method = '' #'whole'
else:
# Check input parameters
try:
opts, args = getopt.getopt(
sys.argv[1:], 'haf:i:l:m:n:o:p:v:w:z:') # define flags
except getopt.GetoptError as err: # check if the arguments are defined
print str(err) # error
usage() # display usage
if not opts:
usage()
for opt, arg in opts: # explore flags
if opt in '-a':
average_all_labels = 1
elif opt in '-f':
path_label = os.path.abspath(arg) # save path of labels folder
elif opt == '-h': # help option
flag_h = 1
elif opt in '-i':
fname_data = arg
elif opt in '-l':
labels_of_interest = arg
elif opt in '-m': # method for metric extraction
method = arg
elif opt in '-n': # filename of the label by which the user wants to normalize
fname_normalizing_label = arg
elif opt in '-o': # output option
fname_output = arg # fname of output file
elif opt in '-p':
adv_param_user = arg
elif opt in '-v':
# vertebral levels option, if the user wants to average the metric across specific vertebral levels
vertebral_levels = arg
elif opt in '-w':
# method used for the normalization by the metric estimation into the normalizing label (see flag -n): 'sbs' for slice-by-slice or 'whole' for normalization after estimation in the whole labels
normalization_method = arg
elif opt in '-z': # slices numbers option
slices_of_interest = arg # save labels numbers
# Display usage with tract parameters by default in case files aren't chosen in arguments inputs
if fname_data == '' or path_label == '' or flag_h:
param.path_label = path_label
usage()
# Check existence of data file
sct.printv('\ncheck existence of input files...', verbose)
sct.check_file_exist(fname_data)
sct.check_folder_exist(path_label)
if fname_normalizing_label:
sct.check_folder_exist(fname_normalizing_label)
# add slash at the end
path_label = sct.slash_at_the_end(path_label, 1)
# Find path to the vertebral labeling file if vertebral levels were specified by the user
if vertebral_levels:
if slices_of_interest: # impossible to select BOTH specific slices and specific vertebral levels
print '\nERROR: You cannot select BOTH vertebral levels AND slice numbers.'
usage()
else:
fname_vertebral_labeling_list = sct.find_file_within_folder(
fname_vertebral_labeling, path_label + '..')
if len(fname_vertebral_labeling_list) > 1:
print color.red + 'ERROR: More than one file named \'' + fname_vertebral_labeling + ' were found in ' + path_label + '. Exit program.' + color.end
sys.exit(2)
elif len(fname_vertebral_labeling_list) == 0:
print color.red + 'ERROR: No file named \'' + fname_vertebral_labeling + ' were found in ' + path_label + '. Exit program.' + color.end
sys.exit(2)
else:
fname_vertebral_labeling = os.path.abspath(
fname_vertebral_labeling_list[0])
# Check input parameters
check_method(method, fname_normalizing_label, normalization_method)
# parse argument for param
if not adv_param_user == '':
adv_param = adv_param_user.replace(' ', '').split(
',') # remove spaces and parse with comma
del adv_param_user # clean variable
# TODO: check integrity of input
# Extract label info
label_id, label_name, label_file = read_label_file(path_label,
param.file_info_label)
nb_labels_total = len(label_id)
# check consistency of label input parameter.
label_id_user, average_all_labels = check_labels(
labels_of_interest, nb_labels_total, average_all_labels,
method) # If 'labels_of_interest' is empty, then
# 'label_id_user' contains the index of all labels in the file info_label.txt
# print parameters
print '\nChecked parameters:'
print ' data ...................... ' + fname_data
print ' folder label .............. ' + path_label
print ' selected labels ........... ' + str(label_id_user)
print ' estimation method ......... ' + method
print ' slices of interest ........ ' + slices_of_interest
print ' vertebral levels .......... ' + vertebral_levels
print ' vertebral labeling file.... ' + fname_vertebral_labeling
print ' advanced parameters ....... ' + str(adv_param)
# Check if the orientation of the data is RPI
orientation_data = get_orientation(fname_data)
# If orientation is not RPI, change to RPI
if orientation_data != 'RPI':
sct.printv(
'\nCreate temporary folder to change the orientation of the NIFTI files into RPI...',
verbose)
path_tmp = sct.slash_at_the_end('tmp.' + time.strftime("%y%m%d%H%M%S"),
1)
sct.create_folder(path_tmp)
# change orientation and load data
sct.printv('\nChange image orientation and load it...', verbose)
data = nib.load(
set_orientation(fname_data, 'RPI',
path_tmp + 'orient_data.nii')).get_data()
# Do the same for labels
sct.printv('\nChange labels orientation and load them...', verbose)
labels = np.empty([nb_labels_total],
dtype=object) # labels(nb_labels_total, x, y, z)
for i_label in range(0, nb_labels_total):
labels[i_label] = nib.load(
set_orientation(path_label + label_file[i_label], 'RPI',
path_tmp + 'orient_' +
label_file[i_label])).get_data()
if fname_normalizing_label: # if the "normalization" option is wanted,
normalizing_label = np.empty(
[1], dtype=object
) # choose this kind of structure so as to keep easily the
# compatibility with the rest of the code (dimensions: (1, x, y, z))
normalizing_label[0] = nib.load(
set_orientation(fname_normalizing_label, 'RPI', path_tmp +
'orient_normalizing_volume.nii')).get_data()
if vertebral_levels: # if vertebral levels were selected,
data_vertebral_labeling = nib.load(
set_orientation(
fname_vertebral_labeling, 'RPI',
path_tmp + 'orient_vertebral_labeling.nii.gz')).get_data()
# Remove the temporary folder used to change the NIFTI files orientation into RPI
sct.printv('\nRemove the temporary folder...', verbose)
status, output = commands.getstatusoutput('rm -rf ' + path_tmp)
else:
# Load image
sct.printv('\nLoad image...', verbose)
data = nib.load(fname_data).get_data()
# Load labels
sct.printv('\nLoad labels...', verbose)
labels = np.empty([nb_labels_total],
dtype=object) # labels(nb_labels_total, x, y, z)
for i_label in range(0, nb_labels_total):
labels[i_label] = nib.load(path_label +
label_file[i_label]).get_data()
if fname_normalizing_label: # if the "normalization" option is wanted,
normalizing_label = np.empty(
[1], dtype=object
) # choose this kind of structure so as to keep easily the
# compatibility with the rest of the code (dimensions: (1, x, y, z))
normalizing_label[0] = nib.load(fname_normalizing_label).get_data(
) # load the data of the normalizing label
if vertebral_levels: # if vertebral levels were selected,
data_vertebral_labeling = nib.load(
fname_vertebral_labeling).get_data()
# Change metric data type into floats for future manipulations (normalization)
data = np.float64(data)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz = data.shape
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# Get dimensions of labels
sct.printv('\nGet dimensions of label...', verbose)
nx_atlas, ny_atlas, nz_atlas = labels[0].shape
sct.printv(
'.. ' + str(nx_atlas) + ' x ' + str(ny_atlas) + ' x ' + str(nz_atlas) +
' x ' + str(nb_labels_total), verbose)
# Check dimensions consistency between atlas and data
if (nx, ny, nz) != (nx_atlas, ny_atlas, nz_atlas):
print '\nERROR: Metric data and labels DO NOT HAVE SAME DIMENSIONS.'
sys.exit(2)
# Update the flag "slices_of_interest" according to the vertebral levels selected by user (if it's the case)
if vertebral_levels:
slices_of_interest, actual_vert_levels, warning_vert_levels = \
get_slices_matching_with_vertebral_levels(data, vertebral_levels, data_vertebral_labeling)
# select slice of interest by cropping data and labels
if slices_of_interest:
data = remove_slices(data, slices_of_interest)
for i_label in range(0, nb_labels_total):
labels[i_label] = remove_slices(labels[i_label],
slices_of_interest)
if fname_normalizing_label: # if the "normalization" option was selected,
normalizing_label[0] = remove_slices(normalizing_label[0],
slices_of_interest)
# if user wants to get unique value across labels, then combine all labels together
if average_all_labels == 1:
sum_labels_user = np.sum(
labels[label_id_user]) # sum the labels selected by user
if method == 'ml' or method == 'map': # in case the maximum likelihood and the average across different labels are wanted
labels_tmp = np.empty([nb_labels_total - len(label_id_user) + 1],
dtype=object)
labels = np.delete(
labels, label_id_user) # remove the labels selected by user
labels_tmp[
0] = sum_labels_user # put the sum of the labels selected by user in first position of the tmp
# variable
for i_label in range(1, len(labels_tmp)):
labels_tmp[i_label] = labels[
i_label -
1] # fill the temporary array with the values of the non-selected labels
labels = labels_tmp # replace the initial labels value by the updated ones (with the summed labels)
del labels_tmp # delete the temporary labels
else: # in other cases than the maximum likelihood, we can remove other labels (not needed for estimation)
labels = np.empty(1, dtype=object)
labels[
0] = sum_labels_user # we create a new label array that includes only the summed labels
if fname_normalizing_label: # if the "normalization" option is wanted
sct.printv('\nExtract normalization values...', verbose)
if normalization_method == 'sbs': # case: the user wants to normalize slice-by-slice
for z in range(0, data.shape[-1]):
normalizing_label_slice = np.empty(
[1], dtype=object
) # in order to keep compatibility with the function
# 'extract_metric_within_tract', define a new array for the slice z of the normalizing labels
normalizing_label_slice[0] = normalizing_label[0][..., z]
metric_normalizing_label = extract_metric_within_tract(
data[..., z], normalizing_label_slice, method, 0)
# estimate the metric mean in the normalizing label for the slice z
if metric_normalizing_label[0][0] != 0:
data[..., z] = data[..., z] / metric_normalizing_label[0][
0] # divide all the slice z by this value
elif normalization_method == 'whole': # case: the user wants to normalize after estimations in the whole labels
metric_mean_norm_label, metric_std_norm_label = extract_metric_within_tract(
data, normalizing_label, method,
param.verbose) # mean and std are lists
# identify cluster for each tract (for use with robust ML)
ml_clusters_array = get_clusters(ml_clusters, labels)
# extract metrics within labels
sct.printv('\nExtract metric within labels...', verbose)
metric_mean, metric_std = extract_metric_within_tract(
data, labels, method, verbose, ml_clusters_array,
adv_param) # mean and std are lists
if fname_normalizing_label and normalization_method == 'whole': # case: user wants to normalize after estimations in the whole labels
metric_mean, metric_std = np.divide(metric_mean,
metric_mean_norm_label), np.divide(
metric_std,
metric_std_norm_label)
# update label name if average
if average_all_labels == 1:
label_name[0] = 'AVERAGED' + ' -'.join(
label_name[i]
for i in label_id_user) # concatenate the names of the
# labels selected by the user if the average tag was asked
label_id_user = [
0
] # update "label_id_user" to select the "averaged" label (which is in first position)
metric_mean = metric_mean[label_id_user]
metric_std = metric_std[label_id_user]
# display metrics
sct.printv('\nEstimation results:', 1)
for i in range(0, metric_mean.size):
sct.printv(
str(label_id_user[i]) + ', ' + str(label_name[label_id_user[i]]) +
': ' + str(metric_mean[i]) + ' +/- ' + str(metric_std[i]), 1,
'info')
# save and display metrics
save_metrics(label_id_user, label_name, slices_of_interest, metric_mean,
metric_std, fname_output, fname_data, method,
fname_normalizing_label, actual_vert_levels,
warning_vert_levels)