def b_spline_centerline(x_centerline, y_centerline, z_centerline):
"""Give a better fitting of the centerline than the method 'spline_centerline' using b-splines"""
points = [[x_centerline[n], y_centerline[n], z_centerline[n]] for n in range(len(x_centerline))]
nurbs = NURBS(3, len(z_centerline)*3, points, nbControl=None, verbose=2) # for the third argument (number of points), give at least len(z_centerline)
# (len(z_centerline)+500 or 1000 is ok)
P = nurbs.getCourbe3D()
x_centerline_fit=P[0]
y_centerline_fit=P[1]
return x_centerline_fit, y_centerline_fit
def b_spline_centerline(x_centerline,y_centerline,z_centerline):
print '\nFitting centerline using B-spline approximation...'
points = [[x_centerline[n],y_centerline[n],z_centerline[n]] for n in range(len(x_centerline))]
nurbs = NURBS(3,3000,points) # BE very careful with the spline order that you choose : if order is too high ( > 4 or 5) you need to set a higher number of Control Points (cf sct_nurbs ). For the third argument (number of points), give at least len(z_centerline)+500 or higher
P = nurbs.getCourbe3D()
x_centerline_fit=P[0]
y_centerline_fit=P[1]
Q = nurbs.getCourbe3D_deriv()
x_centerline_deriv=Q[0]
y_centerline_deriv=Q[1]
z_centerline_deriv=Q[2]
return x_centerline_fit, y_centerline_fit,x_centerline_deriv,y_centerline_deriv,z_centerline_deriv
def b_spline_centerline(x_centerline, y_centerline, z_centerline):
"""Give a better fitting of the centerline than the method 'spline_centerline' using b-splines"""
points = [[x_centerline[n], y_centerline[n], z_centerline[n]]
for n in range(len(x_centerline))]
nurbs = NURBS(
3, len(z_centerline) * 3, points, nbControl=None, verbose=2
) # for the third argument (number of points), give at least len(z_centerline)
# (len(z_centerline)+500 or 1000 is ok)
P = nurbs.getCourbe3D()
x_centerline_fit = P[0]
y_centerline_fit = P[1]
return x_centerline_fit, y_centerline_fit
def writeCenterline(self, output_file_name=None):
# Compute the centerline and write the float coordinates into a txt file
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(self.list_file[0])
# Define output image (size matter)
image_concatenation = self.list_image[0].copy()
image_concatenation.data *= 0
image_output = self.list_image[0].copy()
image_output.data *= 0
# Concatenate all files by addition
for i in range(0, len(self.list_image)):
for s in range(0, nz) :
image_concatenation.data[:,:,s] = image_concatenation.data[:,:,s] + self.list_image[i].data[:,:,s] #* (1/len(self.list_image))
# get center of mass of the centerline/segmentation
sct.printv('\nGet center of mass of the concatenate file...')
z_centerline = [iz for iz in range(0, nz, 1) if image_concatenation.data[:, :, 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(image_concatenation.data[:, :, z_centerline[iz]])
#x_centerline_fit, y_centerline_fit,x_centerline_deriv,y_centerline_deriv,z_centerline_deriv = b_spline_centerline(x_centerline,y_centerline,z_centerline)
points = [[x_centerline[n], y_centerline[n], z_centerline[n]] for n in range(nz_nonz)]
nurbs = NURBS(3, 1000, points)
P = nurbs.getCourbe3D()
x_centerline_fit = P[0]
y_centerline_fit = P[1]
z_centerline_fit = P[2]
# Create output text file
if output_file_name != None :
file_name = output_file_name
else: file_name = 'generated_centerline.txt'
sct.printv('\nWrite text file...')
#file_results = open("../"+file_name, 'w')
file_results = open(file_name, 'w')
for i in range(0, z_centerline_fit.shape[0], 1):
file_results.write(str(int(z_centerline_fit[i])) + ' ' + str(x_centerline_fit[i]) + ' ' + str(y_centerline_fit[i]) + '\n')
file_results.close()
def b_spline_centerline(x_centerline, y_centerline, z_centerline):
print '\nFitting centerline using B-spline approximation...'
points = [[x_centerline[n], y_centerline[n], z_centerline[n]]
for n in range(len(x_centerline))]
nurbs = NURBS(
3, 3000, points
) # BE very careful with the spline order that you choose : if order is too high ( > 4 or 5) you need to set a higher number of Control Points (cf sct_nurbs ). For the third argument (number of points), give at least len(z_centerline)+500 or higher
P = nurbs.getCourbe3D()
x_centerline_fit = P[0]
y_centerline_fit = P[1]
Q = nurbs.getCourbe3D_deriv()
x_centerline_deriv = Q[0]
y_centerline_deriv = Q[1]
z_centerline_deriv = Q[2]
return x_centerline_fit, y_centerline_fit, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv
def compute(self):
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(self.list_file[0])
# Define output image (size matter)
image_concatenation = self.list_image[0].copy()
image_concatenation.data *= 0
image_output = self.list_image[0].copy()
image_output.data *= 0
# Concatenate all files by addition
for i in range(0, len(self.list_image)):
for s in range(0, nz) :
image_concatenation.data[:,:,s] = image_concatenation.data[:,:,s] + self.list_image[i].data[:,:,s] #* (1/len(self.list_image))
# get center of mass of the centerline/segmentation
sct.printv('\nGet center of mass of the concatenate file...')
z_centerline = [iz for iz in range(0, nz, 1) if image_concatenation.data[:, :, 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(image_concatenation.data[:, :, z_centerline[iz]])
points = [[x_centerline[n],y_centerline[n], z_centerline[n]] for n in range(len(z_centerline))]
nurbs = NURBS(3, 1000, points)
P = nurbs.getCourbe3D()
x_centerline_fit = P[0]
y_centerline_fit = P[1]
z_centerline_fit = P[2]
#x_centerline_fit, y_centerline_fit,x_centerline_deriv,y_centerline_deriv,z_centerline_deriv = b_spline_centerline(x_centerline,y_centerline,z_centerline)
for iz in range(0, z_centerline_fit.shape[0], 1):
image_output.data[x_centerline_fit[iz], y_centerline_fit[iz], z_centerline_fit[iz]] = 1
return image_output
def b_spline_nurbs(x,
y,
z,
fname_centerline=None,
degree=3,
point_number=3000,
nbControl=-1,
verbose=1,
all_slices=True):
from math import log
from msct_nurbs import NURBS
twodim = False
if z == None:
twodim = True
"""x.reverse()
y.reverse()
z.reverse()"""
sct.printv('\nFitting centerline using B-spline approximation...', verbose)
if not twodim:
data = [[x[n], y[n], z[n]] for n in range(len(x))]
else:
data = [[x[n], y[n]] for n in range(len(x))]
# if control_points == 0:
# nurbs = NURBS(degree, point_number, data) # BE very careful with the spline order that you choose : if order is too high ( > 4 or 5) you need to set a higher number of Control Points (cf sct_nurbs ). For the third argument (number of points), give at least len(z_centerline)+500 or higher
# else:
# print 'In b_spline_nurbs we get control_point = ', control_points
# nurbs = NURBS(degree, point_number, data, False, control_points)
if nbControl == -1:
centerlineSize = getSize(x, y, z, fname_centerline)
nbControl = 30 * log(centerlineSize, 10) - 42
nbControl = round(nbControl)
nurbs = NURBS(degree,
point_number,
data,
False,
nbControl,
verbose,
all_slices=all_slices,
twodim=twodim)
if not twodim:
P = nurbs.getCourbe3D()
x_fit = P[0]
y_fit = P[1]
z_fit = P[2]
Q = nurbs.getCourbe3D_deriv()
x_deriv = Q[0]
y_deriv = Q[1]
z_deriv = Q[2]
else:
P = nurbs.getCourbe2D()
x_fit = P[0]
y_fit = P[1]
Q = nurbs.getCourbe2D_deriv()
x_deriv = Q[0]
y_deriv = Q[1]
"""x_fit = x_fit[::-1]
y_fit = x_fit[::-1]
z_fit = x_fit[::-1]
x_deriv = x_fit[::-1]
y_deriv = x_fit[::-1]
z_deriv = x_fit[::-1]"""
if verbose == 2:
PC = nurbs.getControle()
PC_x = [p[0] for p in PC]
PC_y = [p[1] for p in PC]
if not twodim:
PC_z = [p[2] for p in PC]
import matplotlib.pyplot as plt
if not twodim:
plt.figure(1)
#ax = plt.subplot(211)
plt.subplot(211)
plt.plot(z, x, 'r.')
plt.plot(z_fit, x_fit)
plt.plot(PC_z, PC_x, 'go')
plt.title("X")
# ax.set_aspect('equal')
plt.xlabel('z')
plt.ylabel('x')
#ay = plt.subplot(212)
plt.subplot(212)
plt.plot(z, y, 'r.')
plt.plot(z_fit, y_fit)
plt.plot(PC_z, PC_y, 'go')
plt.title("Y")
# ay.set_aspect('equal')
plt.xlabel('z')
plt.ylabel('y')
plt.show()
else:
plt.figure(1)
plt.plot(y, x, 'r.')
plt.plot(y_fit, x_fit)
plt.plot(PC_y, PC_x, 'go')
# ax.set_aspect('equal')
plt.xlabel('y')
plt.ylabel('x')
plt.show()
if not twodim:
return x_fit, y_fit, z_fit, x_deriv, y_deriv, z_deriv, nurbs.error_curve_that_last_worked
else:
return x_fit, y_fit, x_deriv, y_deriv, nurbs.error_curve_that_last_worked
def b_spline_nurbs(x, y, z, fname_centerline=None, degree=3, point_number=3000, nbControl=-1, verbose=1):
from math import log
from msct_nurbs import NURBS
"""x.reverse()
y.reverse()
z.reverse()"""
sct.printv('\nFitting centerline using B-spline approximation...', verbose)
data = [[x[n], y[n], z[n]] for n in range(len(x))]
# if control_points == 0:
# nurbs = NURBS(degree, point_number, data) # BE very careful with the spline order that you choose : if order is too high ( > 4 or 5) you need to set a higher number of Control Points (cf sct_nurbs ). For the third argument (number of points), give at least len(z_centerline)+500 or higher
# else:
# print 'In b_spline_nurbs we get control_point = ', control_points
# nurbs = NURBS(degree, point_number, data, False, control_points)
if nbControl == -1:
centerlineSize = getSize(x, y, z, fname_centerline)
nbControl = 30*log(centerlineSize, 10) - 42
nbControl = round(nbControl)
nurbs = NURBS(degree, point_number, data, False, nbControl, verbose)
P = nurbs.getCourbe3D()
x_fit=P[0]
y_fit=P[1]
z_fit=P[2]
Q = nurbs.getCourbe3D_deriv()
x_deriv=Q[0]
y_deriv=Q[1]
z_deriv=Q[2]
"""x_fit = x_fit[::-1]
y_fit = x_fit[::-1]
z_fit = x_fit[::-1]
x_deriv = x_fit[::-1]
y_deriv = x_fit[::-1]
z_deriv = x_fit[::-1]"""
PC = nurbs.getControle()
PC_x = [p[0] for p in PC]
PC_y = [p[1] for p in PC]
PC_z = [p[2] for p in PC]
if verbose == 2:
import matplotlib.pyplot as plt
plt.figure(1)
#ax = plt.subplot(211)
plt.subplot(211)
plt.plot(z, x, 'r.')
plt.plot(z_fit, x_fit)
plt.plot(PC_z,PC_x,'go')
plt.title("X")
#ax.set_aspect('equal')
plt.xlabel('z')
plt.ylabel('x')
#ay = plt.subplot(212)
plt.subplot(212)
plt.plot(z, y, 'r.')
plt.plot(z_fit, y_fit)
plt.plot(PC_z,PC_y,'go')
plt.title("Y")
#ay.set_aspect('equal')
plt.xlabel('z')
plt.ylabel('y')
plt.show()
return x_fit, y_fit, z_fit, x_deriv, y_deriv, z_deriv
def getCenterline(self, type='', output_file_name=None, verbose=0):
# Compute the centerline and save it into a image file of type "type"
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(self.list_file[0])
# Define output image (size matter)
image_concatenation = self.list_image[0].copy()
image_concatenation.data *= 0
image_output = self.list_image[0].copy()
image_output.data *= 0
# Concatenate all files by addition
for i in range(0, len(self.list_image)):
for s in range(0, nz) :
image_concatenation.data[:,:,s] = image_concatenation.data[:,:,s] + self.list_image[i].data[:,:,s] #* (1/len(self.list_image))
print image_concatenation.data[:,:,414]
# get center of mass of the centerline/segmentation
sct.printv('\nGet center of mass of the concatenate file...')
z_centerline = [iz for iz in range(0, nz, 1) if image_concatenation.data[:, :, 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(image_concatenation.data[:, :, z_centerline[iz]])
#x_centerline_fit, y_centerline_fit,x_centerline_deriv,y_centerline_deriv,z_centerline_deriv = b_spline_centerline(x_centerline,y_centerline,z_centerline)
points = [[x_centerline[n], y_centerline[n], z_centerline[n]] for n in range(nz_nonz)]
nurbs = NURBS(3, 1000, points, nbControl=None)
P = nurbs.getCourbe3D()
x_centerline_fit = P[0]
y_centerline_fit = P[1]
z_centerline_fit = P[2]
if verbose==1 :
import matplotlib.pyplot as plt
#Creation of a vector x that takes into account the distance between the labels
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[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,x_centerline, 'ro')
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,y_centerline, 'ro')
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()
for iz in range(0, z_centerline_fit.shape[0], 1):
image_output.data[int(round(x_centerline_fit[iz])), int(round(y_centerline_fit[iz])), z_centerline_fit[iz]] = 1
#image_output.save(type)
file_load = nibabel.load(self.list_file[0])
data = file_load.get_data()
hdr = file_load.get_header()
print '\nWrite NIFTI volumes...'
img = nibabel.Nifti1Image(image_output.data, None, hdr)
if output_file_name != None :
file_name = output_file_name
else: file_name = 'generated_centerline.nii.gz'
nibabel.save(img,file_name)
# to view results
print '\nDone !'
print '\nTo view results, type:'
print 'fslview '+file_name+' &\n'