def test_fbc():
"""Test the FBC measures on a set of fibers"""
# Generate two fibers of 10 points
streamlines = []
for i in range(2):
fiber = np.zeros((10, 3))
for j in range(10):
fiber[j, 0] = j
fiber[j, 1] = i*0.2
fiber[j, 2] = 0
streamlines.append(fiber)
# Create lookup table.
# A fixed set of orientations is used to guarantee deterministic results
D33 = 1.0
D44 = 0.04
t = 1
sphere = Sphere(xyz=np.array([[0.82819078, 0.51050355, 0.23127074],
[-0.10761926, -0.95554309, 0.27450957],
[0.4101745, -0.07154038, 0.90919682],
[-0.75573448, 0.64854889, 0.09082809],
[-0.56874549, 0.01377562, 0.8223982]]))
k = EnhancementKernel(D33, D44, t, orientations=sphere,
force_recompute=True)
# run FBC
fbc = FBCMeasures(streamlines, k, verbose=True)
# get FBC values
fbc_sl_orig, clrs_orig, rfbc_orig = \
fbc.get_points_rfbc_thresholded(0, emphasis=0.01)
# check mean RFBC against tested value
npt.assert_almost_equal(np.mean(rfbc_orig), 1.0500466494329224)
def bundle_coherence(streamlines, affine, k, t1_data=None, interactive=False):
# Compute lookup table
# Apply FBC measures
from dipy.tracking.fbcmeasures import FBCMeasures
fbc = FBCMeasures(streamlines, k)
# Calculate LFBC for original fibers
fbc_sl_orig, clrs_orig, rfbc_orig = \
fbc.get_points_rfbc_thresholded(0, emphasis=0.01)
# Apply a threshold on the RFBC to remove spurious fibers
fbc_sl_thres, clrs_thres, rfbc_thres = \
fbc.get_points_rfbc_thresholded(0.125, emphasis=0.01)
# Visualize the results
from dipy.viz import window, actor
# Create renderer
ren = window.Renderer()
# Original lines colored by LFBC
lineactor = actor.line(fbc_sl_orig, clrs_orig, linewidth=0.2)
ren.add(lineactor)
# Horizontal (axial) slice of T1 data
if t1_data is not None:
vol_actor1 = actor.slicer(t1_data, affine=affine)
vol_actor1.display(z=20)
ren.add(vol_actor1)
# Vertical (sagittal) slice of T1 data
vol_actor2 = actor.slicer(t1_data, affine=affine)
vol_actor2.display(x=35)
ren.add(vol_actor2)
# Show original fibers
ren.set_camera(position=(-264, 285, 155),
focal_point=(0, -14, 9),
view_up=(0, 0, 1))
window.record(ren, n_frames=1, out_path='OR_before.png', size=(900, 900))
if interactive:
window.show(ren)
# Show thresholded fibers
ren.rm(lineactor)
ren.add(actor.line(fbc_sl_thres, clrs_thres, linewidth=0.2))
window.record(ren, n_frames=1, out_path='OR_after.png', size=(900, 900))
if interactive:
window.show(ren)
return k
from dipy.denoise.enhancement_kernel import EnhancementKernel D33 = 1.0 D44 = 0.02 t = 1 k = EnhancementKernel(D33, D44, t) """ The FBC measures are now computed, taking the tractography results and the lookup tables as input. """ # Apply FBC measures from dipy.tracking.fbcmeasures import FBCMeasures fbc = FBCMeasures(streamlines, k) """ After calculating the FBC measures, a threshold can be chosen on the relative FBC (RFBC) in order to remove spurious fibers. Recall that the relative FBC (RFBC) is calculated by the minimum of the moving average LFBC along the fiber. In this example we show the results for threshold 0 (i.e. all fibers are included) and 0.2 (removing the 20 percent most spurious fibers). """ # Calculate LFBC for original fibers fbc_sl_orig, clrs_orig, rfbc_orig = \ fbc.get_points_rfbc_thresholded(0, emphasis=0.01) # Apply a threshold on the RFBC to remove spurious fibers fbc_sl_thres, clrs_thres, rfbc_thres = \
from dipy.denoise.enhancement_kernel import EnhancementKernel D33 = 1.0 D44 = 0.02 t = 1 k = EnhancementKernel(D33, D44, t) """ The FBC measures are now computed, taking the tractography results and the lookup tables as input. """ # Apply FBC measures from dipy.tracking.fbcmeasures import FBCMeasures fbc = FBCMeasures(streamlines, k) """ After calculating the FBC measures, a threshold can be chosen on the relative FBC (RFBC) in order to remove spurious fibers. Recall that the relative FBC (RFBC) is calculated by the minimum of the moving average LFBC along the fiber. In this example we show the results for threshold 0 (i.e. all fibers are included) and 0.2 (removing the 20 percent most spurious fibers). """ # Calculate LFBC for original fibers fbc_sl_orig, clrs_orig, rfbc_orig = \ fbc.get_points_rfbc_thresholded(0, emphasis=0.01) # Apply a threshold on the RFBC to remove spurious fibers fbc_sl_thres, clrs_thres, rfbc_thres = \
'/Structural image/T1w_acpc_dc_restore_brain1.25.nii.gz')
fiber = nibtck.TckFile.load(
'/nfs/s2/userhome/quyukun/workingdir/fiberdata/100206/selectedfiber/'
'100206_IFO_L_prob25.tck')
fiber_data = fiber.streamlines
affine = T1w.affine
img_T1w_data = T1w.get_data()
D33 = 1.0
D44 = 0.02
t = 1
k = EnhancementKernel(D33, D44, t)
# Apply FBC measures
fbc = FBCMeasures(fiber_data, k)
#Calculate LFBC for original fibers
fbc_sl_orig, clrs_orig, rfbc_orig = fbc.get_points_rfbc_thresholded(
0, emphasis=0.01)
# Apply a threshold on the RFBC to remove spurious fibers
fbc_sl_thres, clrs_thres, rfbc_thres = fbc.get_points_rfbc_thresholded(
0.125, emphasis=0.01)
print("The process is already running here.")
# Visualize the results
from dipy.viz import window, actor
# Enables/disables interactive visualization