def __init__(self, fname_im, contrast, fname_seg, path_out, verbose):
self.fname_im = fname_im
self.contrast = contrast
self.fname_seg = fname_seg
self.path_out = path_out
self.verbose = verbose
self.tmp_dir = sct.tmp_create(
verbose=self.verbose) # path to tmp directory
self.orientation_im = get_orientation(Image(
self.fname_im)) # to re-orient the data at the end
self.slice2D_im = sct.extract_fname(
self.fname_im
)[1] + '_midSag.nii' # file used to do the detection, with only one slice
self.dection_map_pmj = sct.extract_fname(
self.fname_im)[1] + '_map_pmj' # file resulting from the detection
# path to the pmj detector
path_sct = os.environ.get("SCT_DIR",
os.path.dirname(os.path.dirname(__file__)))
self.pmj_model = os.path.join(path_sct, 'data', 'pmj_models',
'{}_model'.format(self.contrast))
self.threshold = -0.75 if self.contrast == 't1' else 0.8 # detection map threshold, depends on the contrast
self.fname_out = sct.extract_fname(self.fname_im)[1] + '_pmj.nii.gz'
self.fname_qc = 'qc_pmj.png'
def check_if_rpi(fname):
from sct_image import get_orientation
if not get_orientation(fname, filename=True) == 'RPI':
printv(
'\nERROR: ' + fname +
' is not in RPI orientation. Use sct_image -setorient to reorient your data. Exit program.\n',
1, 'error')
def __init__(self, param=None, param_glcm=None):
self.param = param if param is not None else Param()
self.param_glcm = param_glcm if param_glcm is not None else ParamGLCM()
# create tmp directory
self.tmp_dir = tmp_create(verbose=self.param.verbose) # path to tmp directory
if self.param.dim == 'ax':
self.orientation_extraction = 'RPI'
elif self.param.dim == 'sag':
self.orientation_extraction = 'IPR'
else:
self.orientation_extraction = 'IRP'
# metric_lst=['property_distance_angle']
self.metric_lst = []
for m in list(itertools.product(self.param_glcm.feature.split(','), self.param_glcm.angle.split(','))):
text_name = m[0] if m[0].upper() != 'asm'.upper() else m[0].upper()
self.metric_lst.append(text_name + '_' + str(self.param_glcm.distance) + '_' + str(m[1]))
# dct_im_seg{'im': list_of_axial_slice, 'seg': list_of_axial_masked_slice}
self.dct_im_seg = {'im': None, 'seg': None}
# to re-orient the data at the end if needed
self.orientation_im = get_orientation(Image(self.param.fname_im))
self.fname_metric_lst = {}
def resample_image(fname, suffix='_resampled.nii.gz', binary=False, npx=0.3, npy=0.3, thr=0.0, interpolation='spline'):
"""
Resampling function: add a padding, resample, crop the padding
:param fname: name of the image file to be resampled
:param suffix: suffix added to the original fname after resampling
:param binary: boolean, image is binary or not
:param npx: new pixel size in the x direction
:param npy: new pixel size in the y direction
:param thr: if the image is binary, it will be thresholded at thr (default=0) after the resampling
:param interpolation: type of interpolation used for the resampling
:return: file name after resampling (or original fname if it was already in the correct resolution)
"""
im_in = Image(fname)
orientation = get_orientation(im_in)
if orientation != 'RPI':
im_in = set_orientation(im_in, 'RPI')
im_in.save()
fname = im_in.absolutepath
nx, ny, nz, nt, px, py, pz, pt = im_in.dim
if round(px, 2) != round(npx, 2) or round(py, 2) != round(npy, 2):
name_resample = sct.extract_fname(fname)[1] + suffix
if binary:
interpolation = 'nn'
if nz == 1: # when data is 2d: we convert it to a 3d image in order to avoid nipy problem of conversion nifti-->nipy with 2d data
sct.run(['sct_image', '-i', ','.join([fname, fname]), '-concat', 'z', '-o', fname])
sct.run(['sct_resample', '-i', fname, '-mm', str(npx) + 'x' + str(npy) + 'x' + str(pz), '-o', name_resample, '-x', interpolation])
if nz == 1: # when input data was 2d: re-convert data 3d-->2d
sct.run(['sct_image', '-i', name_resample, '-split', 'z'])
im_split = Image(name_resample.split('.nii.gz')[0] + '_Z0000.nii.gz')
im_split.setFileName(name_resample)
im_split.save()
if binary:
sct.run(['sct_maths', '-i', name_resample, '-bin', str(thr), '-o', name_resample])
if orientation != 'RPI':
im_resample = Image(name_resample)
im_resample = set_orientation(im_resample, orientation)
im_resample.save()
name_resample = im_resample.absolutepath
return name_resample
else:
if orientation != 'RPI':
im_in = set_orientation(im_in, orientation)
im_in.save()
fname = im_in.absolutepath
sct.printv('Image resolution already ' + str(npx) + 'x' + str(npy) + 'xpz')
return fname
def orient2rpi(self):
# save input image orientation
self.orientation = get_orientation(Image(self.fname_mask))
if not self.orientation == 'RPI':
printv('\nOrient input image(s) to RPI orientation...', self.verbose, 'normal')
self._orient(self.fname_mask, 'RPI')
if self.fname_sc is not None:
self._orient(self.fname_sc, 'RPI')
if self.fname_ref is not None:
self._orient(self.fname_ref, 'RPI')
if self.path_template is not None:
self._orient(self.path_levels, 'RPI')
for fname_atlas in self.atlas_roi_lst:
self._orient(fname_atlas, 'RPI')
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, run
from sct_image import get_orientation
# check_file_exist(path, verbose=verbose)
im_file = None
try:
im_file = load(path)
except spatialimages.ImageFileError:
printv('Error: make sure ' + path + ' is an image.', 1, 'error')
self.orientation = get_orientation(path, filename=True)
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 __init__(self, fname_im, contrast, fname_seg, path_out,
quality_control, verbose):
self.fname_im = fname_im
self.contrast = contrast
self.fname_seg = fname_seg
self.path_out = path_out
self.quality_control = quality_control
self.verbose = verbose
self.tmp_dir = tmp_create(
verbose=self.verbose) # path to tmp directory
self.orientation_im = get_orientation(Image(
self.fname_im)) # to re-orient the data at the end
self.slice2D_im = extract_fname(
self.fname_im
)[1] + '_midSag.nii' # file used to do the detection, with only one slice
self.dection_map_pmj = extract_fname(
self.fname_im)[1] + '_map_pmj' # file resulting from the detection
# path to the pmj detector
self.pmj_model = os.path.join(
commands.getstatusoutput('echo $SCT_DIR')[1], 'data/pmj_models',
'{}_model'.format(self.contrast))
self.threshold = -0.75 if self.contrast == 't1' else 0.8 # detection map threshold, depends on the contrast
self.fname_out = extract_fname(self.fname_im)[1] + '_pmj.nii.gz'
self.fname_qc = 'qc_pmj.png'
def process(self):
# preprocessing
os.chdir(self.tmp_dir)
im_target = Image(self.original_target)
im_sc_seg = Image(self.original_sc_seg)
self.original_header = im_target.hdr
self.original_orientation = im_target.orientation
index_x = self.original_orientation.find('R') if 'R' in self.original_orientation else self.original_orientation.find('L')
index_y = self.original_orientation.find('P') if 'P' in self.original_orientation else self.original_orientation.find('A')
index_z = self.original_orientation.find('I') if 'I' in self.original_orientation else self.original_orientation.find('S')
# resampling of the images
nx, ny, nz, nt, px, py, pz, pt = im_target.dim
pix_dim = [px, py, pz]
self.original_px = pix_dim[index_x]
self.original_py = pix_dim[index_y]
if round(self.original_px, 2) != self.resample_to or round(self.original_py, 2) != self.resample_to:
self.t2star = resample_image(self.original_target, npx=self.resample_to, npy=self.resample_to)
self.sc_seg = resample_image(self.original_sc_seg, binary=True, npx=self.resample_to, npy=self.resample_to)
# denoising (optional)
im_target = Image(self.t2star)
if self.denoising:
from sct_maths import denoise_ornlm
im_target.data = denoise_ornlm(im_target.data)
im_target.save()
self.t2star = im_target.file_name + im_target.ext
box_size = int(22.5/self.resample_to)
# Pad in case the spinal cord is too close to the edges
pad_size = box_size/2 + 2
self.pad = [str(pad_size)]*3
self.pad[index_z] = str(0)
t2star_pad = sct.add_suffix(self.t2star, '_pad')
sc_seg_pad = sct.add_suffix(self.sc_seg, '_pad')
sct.run('sct_image -i '+self.t2star+' -pad '+self.pad[0]+','+self.pad[1]+','+self.pad[2]+' -o '+t2star_pad)
sct.run('sct_image -i '+self.sc_seg+' -pad '+self.pad[0]+','+self.pad[1]+','+self.pad[2]+' -o '+sc_seg_pad)
self.t2star = t2star_pad
self.sc_seg = sc_seg_pad
# put data in RPI
t2star_rpi = sct.add_suffix(self.t2star, '_RPI')
sc_seg_rpi = sct.add_suffix(self.sc_seg, '_RPI')
sct.run('sct_image -i '+self.t2star+' -setorient RPI -o '+t2star_rpi)
sct.run('sct_image -i '+self.sc_seg+' -setorient RPI -o '+sc_seg_rpi)
self.t2star = t2star_rpi
self.sc_seg = sc_seg_rpi
self.square_mask, self.processed_target = crop_t2_star(self.t2star, self.sc_seg, box_size=box_size)
if self.t2 is not None:
self.fname_level = compute_level_file(self.t2star, self.sc_seg, self.t2, self.t2_seg, self.t2_landmarks)
elif self.fname_level is not None:
level_orientation = get_orientation(self.fname_level, filename=True)
if level_orientation != 'IRP':
self.fname_level = set_orientation(self.fname_level, 'IRP', filename=True)
os.chdir('..')
def main():
# initialization
fname_mask = ''
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_data = arguments['-i']
fname_mask = arguments['-m']
vert_label_fname = arguments["-vertfile"]
vert_levels = arguments["-vert"]
slices_of_interest = arguments["-z"]
index_vol = arguments['-vol']
method = arguments["-method"]
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
# Check if data are in RPI
input_im = Image(fname_data)
input_orient = get_orientation(input_im)
# If orientation is not RPI, change to RPI
if input_orient != 'RPI':
sct.printv(
'\nCreate temporary folder to change the orientation of the NIFTI files into RPI...',
verbose)
path_tmp = sct.tmp_create()
# change orientation and load data
sct.printv('\nChange input image orientation and load it...', verbose)
input_im_rpi = orientation(input_im,
ori='RPI',
set=True,
fname_out=os.path.join(
path_tmp, "input_RPI.nii"))
input_data = input_im_rpi.data
# Do the same for the mask
sct.printv('\nChange mask orientation and load it...', verbose)
mask_im_rpi = orientation(Image(fname_mask),
ori='RPI',
set=True,
fname_out=os.path.join(
path_tmp, "mask_RPI.nii"))
mask_data = mask_im_rpi.data
# Do the same for vertebral labeling if present
if vert_levels != 'None':
sct.printv(
'\nChange vertebral labeling file orientation and load it...',
verbose)
vert_label_im_rpi = orientation(Image(vert_label_fname),
ori='RPI',
set=True,
fname_out=os.path.join(
path_tmp,
"vert_labeling_RPI.nii"))
vert_labeling_data = vert_label_im_rpi.data
# Remove the temporary folder used to change the NIFTI files orientation into RPI
if remove_temp_files:
sct.printv('\nRemove the temporary folder...', verbose)
sct.rmtree(path_tmp, True)
else:
# Load data
sct.printv('\nLoad data...', verbose)
input_data = input_im.data
mask_data = Image(fname_mask).data
if vert_levels != 'None':
vert_labeling_data = Image(vert_label_fname).data
sct.printv('\tDone.', verbose)
# Get slices corresponding to vertebral levels
if vert_levels != 'None':
from sct_extract_metric import get_slices_matching_with_vertebral_levels
slices_of_interest, actual_vert_levels, warning_vert_levels = get_slices_matching_with_vertebral_levels(
mask_data, vert_levels, vert_labeling_data, verbose)
# Remove slices that were not selected
if slices_of_interest == 'None':
slices_of_interest = '0:' + str(mask_data.shape[2] - 1)
slices_boundary = slices_of_interest.split(':')
slices_of_interest_list = range(int(slices_boundary[0]),
int(slices_boundary[1]) + 1)
# Crop
input_data = input_data[:, :, slices_of_interest_list, :]
mask_data = mask_data[:, :, slices_of_interest_list]
# if user selected all slices (-vol -1), then assign index_vol
if index_vol[0] == -1:
index_vol = range(0, input_data.shape[3], 1)
# Get signal and noise
indexes_roi = np.where(mask_data == 1)
if method == 'mult':
# get voxels in ROI to obtain a (x*y*z)*t 2D matrix
input_data_in_roi = input_data[indexes_roi]
# compute signal and STD across by averaging across time
signal = np.mean(input_data_in_roi[:, index_vol])
std_input_temporal = np.std(input_data_in_roi[:, index_vol], 1)
noise = np.mean(std_input_temporal)
elif method == 'diff':
# if user did not select two volumes, then exit with error
if not len(index_vol) == 2:
sct.printv(
'ERROR: ' + str(len(index_vol)) +
' volumes were specified. Method "diff" should be used with exactly two volumes.',
1, 'error')
data_1 = input_data[:, :, :, index_vol[0]]
data_2 = input_data[:, :, :, index_vol[1]]
# compute voxel-average of voxelwise sum
signal = np.mean(np.add(data_1[indexes_roi], data_2[indexes_roi]))
# compute voxel-STD of voxelwise substraction, multiplied by sqrt(2) as described in equation 7 of Dietrich et al.
noise = np.std(np.subtract(data_1[indexes_roi],
data_2[indexes_roi])) * np.sqrt(2)
# compute SNR
SNR = signal / noise
# Display result
sct.printv('\nSNR_' + method + ' = ' + str(SNR) + '\n', type='info')
def main(fname_anat, fname_centerline, degree_poly, centerline_fitting, interp, remove_temp_files, verbose):
# 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'
# 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
im_anat = Image(fname_anat)
input_image_orientation = get_orientation(im_anat)
# Reorient input data into RL PA IS orientation
im_centerline = Image(fname_centerline)
im_anat_orient = set_orientation(im_anat, 'RPI')
im_anat_orient.setFileName('tmp.anat_orient.nii')
im_centerline_orient = set_orientation(im_centerline, 'RPI')
im_centerline_orient.setFileName('tmp.centerline_orient.nii')
# Open centerline
#==========================================================================================
print '\nGet dimensions of input centerline...'
nx, ny, nz, nt, px, py, pz, pt = im_centerline_orient.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...'
data = im_centerline_orient.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 == 'nurbs':
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...'
im_anat_orient_split_list = split_data(im_anat_orient, 2)
file_anat_split = []
for im in im_anat_orient_split_list:
file_anat_split.append(im.absolutepath)
im.save()
# 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 glob import glob
im_to_concat_list = [Image(fname) for fname in glob('tmp.anat_orient_fit_Z*.nii')]
im_concat_out = concat_data(im_to_concat_list, 2)
im_concat_out.setFileName('tmp.anat_orient_fit.nii')
im_concat_out.save()
# 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...'
fname_anat_fit_orient = set_orientation(im_concat_out.absolutepath, input_image_orientation, filename=True)
move(fname_anat_fit_orient, '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 check_if_rpi(fname):
from sct_image import get_orientation
if not get_orientation(fname, filename=True) == 'RPI':
printv('\nERROR: '+fname+' is not in RPI orientation. Use sct_image -setorient to reorient your data. Exit program.\n', 1, 'error')
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_image 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, filename=True)
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] = px * x_trans[k]
data_warp[i, j, k, 0, 1] = py * 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 get_centerline_from_point(input_image, point_file, gap=4, gaussian_kernel=4, remove_tmp_files=1):
# Initialization
fname_anat = input_image
fname_point = point_file
slice_gap = gap
remove_tmp_files = remove_tmp_files
gaussian_kernel = gaussian_kernel
start_time = time()
verbose = 1
# 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
# 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, filename=True)
# 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.'+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
im_anat = convert('tmp.anat'+ext_anat, 'tmp.anat.nii')
im_point = convert('tmp.point'+ext_point, 'tmp.point.nii')
# Reorient input anatomical volume into RL PA IS orientation
print '\nReorient input volume to RL PA IS orientation...'
set_orientation(im_anat, 'RPI')
im_anat.setFileName('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(im_point, 'RPI')
im_point.setFileName('tmp.point_orient.nii')
# Get image dimensions
print '\nGet image dimensions...'
nx, ny, nz, nt, px, py, pz, pt = Image('tmp.anat_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'
# Split input volume
print '\nSplit input volume...'
im_anat_split_list = split_data(im_anat, 2)
file_anat_split = []
for im in im_anat_split_list:
file_anat_split.append(im.absolutepath)
im.save()
im_point_split_list = split_data(im_point, 2)
file_point_split = []
for im in im_point_split_list:
file_point_split.append(im.absolutepath)
im.save()
# Extract coordinates of input point
data_point = Image('tmp.point_orient.nii').data
x_init, y_init, z_init = unravel_index(data_point.argmax(), data_point.shape)
sct.printv('Coordinates of input point: ('+str(x_init)+', '+str(y_init)+', '+str(z_init)+')', verbose)
# Create 2D gaussian mask
sct.printv('\nCreate gaussian mask from point...', verbose)
xx, yy = mgrid[:nx, :ny]
mask2d = zeros((nx, ny))
radius = round(float(gaussian_kernel+1)/2) # add 1 because the radius includes the center.
sigma = float(radius)
mask2d = exp(-(((xx-x_init)**2)/(2*(sigma**2)) + ((yy-y_init)**2)/(2*(sigma**2))))
# Save mask to 2d file
file_mask_split = ['tmp.mask_orient_Z'+str(z).zfill(4) for z in range(0, nz, 1)]
nii_mask2d = Image('tmp.anat_orient_Z0000.nii')
nii_mask2d.data = mask2d
nii_mask2d.setFileName(file_mask_split[z_init]+'.nii')
nii_mask2d.save()
# 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 < nz:
# 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 = 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 = polyfit(z_centerline, x_centerline, deg=param.deg_poly)
polyx = poly1d(coeffsx)
x_centerline_fit = polyval(polyx, z_centerline)
# calculate RMSE
rmse = linalg.norm(x_centerline_fit-x_centerline)/sqrt( len(x_centerline) )
# calculate max absolute error
max_abs = max(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 = polyfit(z_centerline, y_centerline, deg=param.deg_poly)
polyy = poly1d(coeffsy)
y_centerline_fit = polyval(polyy, z_centerline)
# calculate RMSE
rmse = linalg.norm(y_centerline_fit-y_centerline)/sqrt( len(y_centerline) )
# calculate max absolute error
max_abs = max( 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 = polyval(polyx, z_centerline_full)
y_centerline_fit_full = 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
savetxt('tmp.centerline_polycoeffs_x.txt',coeffsx)
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...'
im_anat_list = [Image(fname) for fname in glob.glob('tmp.anat_orient_fit_z*.nii')]
im_anat_concat = concat_data(im_anat_list, 2)
im_anat_concat.setFileName('tmp.anat_orient_fit.nii')
im_anat_concat.save()
im_mask_list = [Image(fname) for fname in glob.glob('tmp.mask_orient_fit_z*.nii')]
im_mask_concat = concat_data(im_mask_list, 2)
im_mask_concat.setFileName('tmp.mask_orient_fit.nii')
im_mask_concat.save()
im_point_list = [Image(fname) for fname in glob.glob('tmp.point_orient_fit_z*.nii')]
im_point_concat = concat_data(im_point_list, 2)
im_point_concat.setFileName('tmp.point_orient_fit.nii')
im_point_concat.save()
# Copy header geometry from input data
print '\nCopy header geometry from input data...'
im_anat = Image('tmp.anat_orient.nii')
im_anat_orient_fit = Image('tmp.anat_orient_fit.nii')
im_mask_orient_fit = Image('tmp.mask_orient_fit.nii')
im_point_orient_fit = Image('tmp.point_orient_fit.nii')
im_anat_orient_fit = copy_header(im_anat, im_anat_orient_fit)
im_mask_orient_fit = copy_header(im_anat, im_mask_orient_fit)
im_point_orient_fit = copy_header(im_anat, im_point_orient_fit)
for im in [im_anat_orient_fit, im_mask_orient_fit, im_point_orient_fit]:
im.save()
# 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
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, error_exit='warning')
# 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() - start_time
print '\nFinished! \n\tGenerated file: '+fname_output_centerline+'\n\tElapsed time: '+str(int(round(elapsed_time)))+'s\n'
def get_centerline_from_labels(fname_in, list_fname_labels, param, output_file_name=None, remove_temp_files=1, verbose=0):
path, file, ext = sct.extract_fname(fname_in)
# create temporary folder
path_tmp = sct.slash_at_the_end('tmp.'+strftime('%y%m%d%H%M%S'), 1)
sct.run('mkdir '+path_tmp)
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder...', verbose)
sct.run('sct_convert -i '+fname_in+' -o '+path_tmp+'data.nii')
file_labels = []
for i in range(len(list_fname_labels)):
file_labels.append('labels_'+str(i)+'.nii.gz')
sct.run('sct_convert -i '+list_fname_labels[i]+' -o '+path_tmp+file_labels[i])
# go to tmp folder
os.chdir(path_tmp)
## Concatenation of the files
# Concatenation : sum of matrices
file_0 = Image('data.nii')
data_concatenation = file_0.data
hdr_0 = file_0.hdr
orientation_file_0 = get_orientation(file_0)
if len(list_fname_labels) > 0:
for i in range(0, len(list_fname_labels)):
orientation_file_temp = get_orientation(file_labels[i], filename=True)
if orientation_file_0 != orientation_file_temp :
print 'ERROR: The files ', fname_in, ' and ', file_labels[i], ' are not in the same orientation. Use sct_image -setorient to change the orientation of a file.'
sys.exit(2)
file_temp = load(file_labels[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
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, 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
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.tmp_create(param.verbose)
# )sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
# sct.run('mkdir '+path_tmp, param.verbose)
sct.printv('\nCheck orientation...', param.verbose)
orientation_input = get_orientation(Image(param.fname_data))
sct.printv('.. '+orientation_input, param.verbose)
reorient_coordinates = False
# copy input data to tmp folder
convert(param.fname_data, path_tmp+'data.nii')
if method_type == 'centerline':
convert(method_val, path_tmp+'centerline.nii.gz')
if method_type == 'point':
convert(method_val, path_tmp+'point.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# reorient to RPI
sct.printv('\nReorient to RPI...', param.verbose)
# if not orientation_input == 'RPI':
sct.run('sct_image -i data.nii -o data_RPI.nii -setorient RPI -v 0', verbose=False)
if method_type == 'centerline':
sct.run('sct_image -i centerline.nii.gz -o centerline_RPI.nii.gz -setorient RPI -v 0', verbose=False)
if method_type == 'point':
sct.run('sct_image -i point.nii.gz -o point_RPI.nii.gz -setorient RPI -v 0', verbose=False)
#
# if method_type == 'centerline':
# orientation_centerline = get_orientation_3d(method_val, filename=True)
# if not orientation_centerline == 'RPI':
# sct.run('sct_image -i ' + method_val + ' -o ' + path_tmp + 'centerline.nii.gz' + ' -setorient RPI -v 0', verbose=False)
# else:
# convert(method_val, path_tmp+'centerline.nii.gz')
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data_RPI.nii').dim
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
nii = Image('data_RPI.nii')
data3d = nii.data[:,:,:,0]
nii.data = data3d
nii.save()
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)
# TODO: change this way to remove dependence to sct.run. ProcessLabels.display_voxel returns list of coordinates
status, output = sct.run('sct_label_utils -i point_RPI.nii.gz -display', 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_RPI.nii.gz'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line('data_RPI.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
spacing = hdr.structarr['pixdim']
data_centerline = centerline.get_data() # get centerline
z_centerline_not_null = [iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()]
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
if iz in z_centerline_not_null:
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(numpy.array(data_centerline[:, :, iz]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
if iz not in z_centerline_not_null:
# write an empty nifty volume
img = nibabel.Nifti1Image(data_centerline[:, :, iz], None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
else:
center = numpy.array([cx[iz], cy[iz]])
mask2d = create_mask2d(center, param.shape, param.size, nx, ny, even=param.even, spacing=spacing)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask+str(iz)+'.nii'))
# merge along Z
# cmd = 'fslmerge -z mask '
# CHANGE THAT CAN IMPACT SPEED:
# related to issue #755, we cannot open more than 256 files at one time.
# to solve this issue, we do not open more than 100 files
'''
im_list = []
im_temp = []
for iz in range(nz_not_null):
if iz != 0 and iz % 100 == 0:
im_temp.append(concat_data(im_list, 2))
im_list = [Image(file_mask + str(iz) + '.nii')]
else:
im_list.append(Image(file_mask+str(iz)+'.nii'))
if im_temp:
im_temp.append(concat_data(im_list, 2))
im_out = concat_data(im_temp, 2, no_expand=True)
else:
im_out = concat_data(im_list, 2)
'''
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2)
im_out.setFileName('mask_RPI.nii.gz')
im_out.save()
# reorient if necessary
# if not orientation_input == 'RPI':
sct.run('sct_image -i mask_RPI.nii.gz -o mask.nii.gz -setorient ' + orientation_input, param.verbose)
# copy header input --> mask
im_dat = Image('data.nii')
im_mask = Image('mask.nii.gz')
im_mask = copy_header(im_dat, im_mask)
im_mask.save()
# 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, error_exit='warning')
# 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 create_mask():
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# check if orientation is RPI
sct.printv('\nCheck if orientation is RPI...', param.verbose)
ori = get_orientation(param.fname_data, filename=True)
if not ori == 'RPI':
sct.printv('\nERROR in '+os.path.basename(__file__)+': Orientation of input image should be RPI. Use sct_image -setorient to put your image in RPI.\n', 1, 'error')
# 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
sct.printv('\nCopying input data to tmp folder and convert to nii...', param.verbose)
convert(param.fname_data, path_tmp+'data.nii')
# sct.run('cp '+param.fname_data+' '+path_tmp+'data'+ext_data, param.verbose)
if method_type == 'centerline':
convert(method_val, path_tmp+'centerline.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data.nii').dim
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
nii = Image('data.nii')
data3d = nii.data[:,:,:,0]
nii.data = data3d
nii.save()
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+' -p 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, param.even)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask+str(iz)+'.nii'))
# merge along Z
# cmd = 'fslmerge -z mask '
im_list = []
for iz in range(nz):
im_list.append(Image(file_mask+str(iz)+'.nii'))
im_out = concat_data(im_list, 2)
im_out.setFileName('mask.nii.gz')
im_out.save()
# copy geometry
im_dat = Image('data.nii')
im_mask = Image('mask.nii.gz')
im_mask = copy_header(im_dat, im_mask)
im_mask.save()
# 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, error_exit='warning')
# 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 create_mask():
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, 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
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.tmp_create(param.verbose)
# )sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
# sct.run('mkdir '+path_tmp, param.verbose)
sct.printv('\nCheck orientation...', param.verbose)
orientation_input = get_orientation(Image(param.fname_data))
sct.printv('.. ' + orientation_input, param.verbose)
reorient_coordinates = False
# copy input data to tmp folder
convert(param.fname_data, path_tmp + 'data.nii')
if method_type == 'centerline':
convert(method_val, path_tmp + 'centerline.nii.gz')
if method_type == 'point':
convert(method_val, path_tmp + 'point.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# reorient to RPI
sct.printv('\nReorient to RPI...', param.verbose)
# if not orientation_input == 'RPI':
sct.run('sct_image -i data.nii -o data_RPI.nii -setorient RPI -v 0',
verbose=False)
if method_type == 'centerline':
sct.run(
'sct_image -i centerline.nii.gz -o centerline_RPI.nii.gz -setorient RPI -v 0',
verbose=False)
if method_type == 'point':
sct.run(
'sct_image -i point.nii.gz -o point_RPI.nii.gz -setorient RPI -v 0',
verbose=False)
#
# if method_type == 'centerline':
# orientation_centerline = get_orientation_3d(method_val, filename=True)
# if not orientation_centerline == 'RPI':
# sct.run('sct_image -i ' + method_val + ' -o ' + path_tmp + 'centerline.nii.gz' + ' -setorient RPI -v 0', verbose=False)
# else:
# convert(method_val, path_tmp+'centerline.nii.gz')
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data_RPI.nii').dim
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
nii = Image('data_RPI.nii')
data3d = nii.data[:, :, :, 0]
nii.data = data3d
nii.save()
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)
# TODO: change this way to remove dependence to sct.run. ProcessLabels.display_voxel returns list of coordinates
status, output = sct.run(
'sct_label_utils -i point_RPI.nii.gz -display', 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_RPI.nii.gz'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line('data_RPI.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
spacing = hdr.structarr['pixdim']
data_centerline = centerline.get_data() # get centerline
# if data is 2D, reshape with empty third dimension
if len(data_centerline.shape) == 2:
data_centerline_shape = list(data_centerline.shape)
data_centerline_shape.append(1)
data_centerline = data_centerline.reshape(data_centerline_shape)
z_centerline_not_null = [
iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()
]
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
if iz in z_centerline_not_null:
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(
numpy.array(data_centerline[:, :, iz]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
if iz not in z_centerline_not_null:
# write an empty nifty volume
img = nibabel.Nifti1Image(data_centerline[:, :, iz], None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
else:
center = numpy.array([cx[iz], cy[iz]])
mask2d = create_mask2d(center,
param.shape,
param.size,
nx,
ny,
even=param.even,
spacing=spacing)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
# merge along Z
# cmd = 'fslmerge -z mask '
# CHANGE THAT CAN IMPACT SPEED:
# related to issue #755, we cannot open more than 256 files at one time.
# to solve this issue, we do not open more than 100 files
'''
im_list = []
im_temp = []
for iz in range(nz_not_null):
if iz != 0 and iz % 100 == 0:
im_temp.append(concat_data(im_list, 2))
im_list = [Image(file_mask + str(iz) + '.nii')]
else:
im_list.append(Image(file_mask+str(iz)+'.nii'))
if im_temp:
im_temp.append(concat_data(im_list, 2))
im_out = concat_data(im_temp, 2, no_expand=True)
else:
im_out = concat_data(im_list, 2)
'''
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2)
im_out.setFileName('mask_RPI.nii.gz')
im_out.save()
# reorient if necessary
# if not orientation_input == 'RPI':
sct.run(
'sct_image -i mask_RPI.nii.gz -o mask.nii.gz -setorient ' +
orientation_input, param.verbose)
# copy header input --> mask
im_dat = Image('data.nii')
im_mask = Image('mask.nii.gz')
im_mask = copy_header(im_dat, im_mask)
im_mask.save()
# 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, error_exit='warning')
# 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 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_image import get_orientation
self.orientation = get_orientation(self)
# 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, 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'
# update dim
# http://math.stackexchange.com/questions/122916/what-is-the-inverse-cycle-of-permutation
dim_temp = list(self.dim)
dim_temp[0] = self.dim[[i for i, x in enumerate(perm) if x == 0][0]] # nx
dim_temp[1] = self.dim[[i for i, x in enumerate(perm) if x == 1][0]] # ny
dim_temp[2] = self.dim[[i for i, x in enumerate(perm) if x == 2][0]] # nz
dim_temp[4] = self.dim[[i for i, x in enumerate(perm) if x == 0][0]+4] # px
dim_temp[5] = self.dim[[i for i, x in enumerate(perm) if x == 1][0]+4] # py
dim_temp[6] = self.dim[[i for i, x in enumerate(perm) if x == 2][0]+4] # pz
self.dim = tuple(dim_temp)
# update orientation
self.orientation = orientation
return raw_orientation
