def test_spike():
""" Test if a convolution with a delta spike is equal to the kernel
saved in the lookup table."""
# create kernel
D33 = 1.0
D44 = 0.04
t = 1
num_orientations = 5
k = EnhancementKernel(D33,
D44,
t,
orientations=num_orientations,
force_recompute=True)
# create a delta spike
numorientations = k.get_orientations().shape[0]
spike = np.zeros((7, 7, 7, numorientations), dtype=np.float64)
spike[3, 3, 3, 0] = 1
# convolve kernel with delta spike
csd_enh = convolve_sf(spike, k, test_mode=True, normalize=False)
# check if kernel matches with the convolved delta spike
totalsum = 0.0
for i in range(0, numorientations):
totalsum += np.sum(np.array(k.get_lookup_table())[i, 0, :, :, :] - \
np.array(csd_enh)[:, :, :, i])
npt.assert_equal(totalsum, 0.0)
def test_normalization():
""" Test the normalization routine applied after a convolution"""
# create kernel
D33 = 1.0
D44 = 0.04
t = 1
num_orientations = 5
k = EnhancementKernel(D33,
D44,
t,
orientations=num_orientations,
force_recompute=True)
# create a constant dataset
numorientations = k.get_orientations().shape[0]
spike = np.ones((7, 7, 7, numorientations), dtype=np.float64)
# convert dataset to SH
spike_sh = sf_to_sh(spike, k.get_sphere(), sh_order=8)
# convolve kernel with delta spike and apply normalization
csd_enh = convolve(spike_sh, k, sh_order=8, test_mode=True, normalize=True)
# convert dataset to DSF
csd_enh_dsf = sh_to_sf(csd_enh,
k.get_sphere(),
sh_order=8,
basis_type=None)
# test if the normalization is performed correctly
npt.assert_almost_equal(np.amax(csd_enh_dsf), np.amax(spike))
def test_spike():
""" Test if a convolution with a delta spike is equal to the kernel
saved in the lookup table."""
# create kernel
D33 = 1.0
D44 = 0.04
t = 1
num_orientations = 5
k = EnhancementKernel(D33, D44, t, orientations=num_orientations, force_recompute=True)
# create a delta spike
numorientations = k.get_orientations().shape[0]
spike = np.zeros((7, 7, 7, numorientations), dtype=np.float64)
spike[3, 3, 3, 0] = 1
# convolve kernel with delta spike
csd_enh = convolve_sf(spike, k, test_mode=True, normalize=False)
# check if kernel matches with the convolved delta spike
totalsum = 0.0
for i in range(0, numorientations):
totalsum += np.sum(np.array(k.get_lookup_table())[i, 0, :, :, :] - \
np.array(csd_enh)[:, :, :, i])
npt.assert_equal(totalsum, 0.0)
def test_enhancement_kernel():
""" Test if the kernel values are correct by comparison against the values
originally calculated by implementation in Mathematica, and at the same time
checks the symmetry of the kernel."""
D33 = 1.0
D44 = 0.04
t = 1
k = EnhancementKernel(D33, D44, t, orientations=0, force_recompute=True)
y = np.array([0., 0., 0.])
v = np.array([0., 0., 1.])
orientationlist = [[0., 0., 1.], [-0.0527864, 0.688191, 0.723607],
[-0.67082, -0.16246, 0.723607],
[-0.0527864, -0.688191, 0.723607],
[0.638197, -0.262866, 0.723607],
[0.831052, 0.238856, 0.502295],
[0.262866, -0.809017, -0.525731],
[0.812731, 0.295242, -0.502295],
[-0.029644, 0.864188, -0.502295],
[-0.831052, 0.238856, -0.502295],
[-0.638197, -0.262866, -0.723607],
[-0.436009, 0.864188, -0.251148],
[-0.687157, -0.681718, 0.251148],
[0.67082, -0.688191, 0.276393],
[0.67082, 0.688191, 0.276393],
[0.947214, 0.16246, -0.276393],
[-0.861803, -0.425325, -0.276393]]
positionlist = [[-0.108096, 0.0412229, 0.339119],
[0.220647, -0.422053, 0.427524],
[-0.337432, -0.0644619, -0.340777],
[0.172579, -0.217602, -0.292446],
[-0.271575, -0.125249, -0.350906],
[-0.483807, 0.326651, 0.191993],
[-0.480936, -0.0718426, 0.33202],
[0.497193, -0.00585659, -0.251344],
[0.237737, 0.013634, -0.471988],
[0.367569, -0.163581, 0.0723955],
[0.47859, -0.143252, 0.318579],
[-0.21474, -0.264929, -0.46786],
[-0.0684234, 0.0342464, 0.0942475],
[0.344272, 0.423119, -0.303866],
[0.0430714, 0.216233, -0.308475],
[0.386085, 0.127333, 0.0503609],
[0.334723, 0.071415, 0.403906]]
kernelvalues = [
0.10701063104295713, 0.0030052117308328923, 0.003125410084676201,
0.0031765819772012613, 0.003127254657020615, 0.0001295130396491743,
6.882352014430076e-14, 1.3821277371353332e-13, 1.3951939946082493e-13,
1.381612071786285e-13, 5.0861109163441125e-17, 1.0722120295517027e-10,
2.425145934791457e-6, 3.557919265806602e-6, 3.6669510385105265e-6,
5.97473789679846e-11, 6.155412262223178e-11
]
for p in range(len(orientationlist)):
r = np.array(orientationlist[p])
x = np.array(positionlist[p])
npt.assert_almost_equal(k.evaluate_kernel(x, y, r, v), kernelvalues[p])
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 test_enhancement_kernel():
""" Test if the kernel values are correct by comparison against the values
originally calculated by implementation in Mathematica, and at the same time
checks the symmetry of the kernel."""
D33 = 1.0
D44 = 0.04
t = 1
k = EnhancementKernel(D33, D44, t, orientations=0, force_recompute=True)
y = np.array([0., 0., 0.])
v = np.array([0., 0., 1.])
orientationlist=[[0., 0., 1.], [-0.0527864, 0.688191, 0.723607], \
[-0.67082, -0.16246, 0.723607], [-0.0527864, -0.688191, 0.723607], \
[0.638197, -0.262866, 0.723607], [0.831052, 0.238856, 0.502295], \
[0.262866, -0.809017, -0.525731], [0.812731, 0.295242, -0.502295], \
[-0.029644, 0.864188, -0.502295], [-0.831052, 0.238856, -0.502295], \
[-0.638197, -0.262866, -0.723607], [-0.436009, 0.864188, -0.251148], \
[-0.687157, -0.681718, 0.251148], [0.67082, -0.688191, 0.276393], \
[0.67082, 0.688191, 0.276393], [0.947214, 0.16246, -0.276393], \
[-0.861803, -0.425325, -0.276393]]
positionlist= [[-0.108096, 0.0412229, 0.339119], [0.220647, -0.422053, 0.427524], \
[-0.337432, -0.0644619, -0.340777], [0.172579, -0.217602, -0.292446], \
[-0.271575, -0.125249, -0.350906], [-0.483807, 0.326651, 0.191993], \
[-0.480936, -0.0718426, 0.33202], [0.497193, -0.00585659, -0.251344], \
[0.237737, 0.013634, -0.471988], [0.367569, -0.163581, 0.0723955], \
[0.47859, -0.143252, 0.318579], [-0.21474, -0.264929, -0.46786], \
[-0.0684234, 0.0342464, 0.0942475], [0.344272, 0.423119, -0.303866], \
[0.0430714, 0.216233, -0.308475], [0.386085, 0.127333, 0.0503609], \
[0.334723, 0.071415, 0.403906]]
kernelvalues = [0.10701063104295713, 0.0030052117308328923, 0.003125410084676201, \
0.0031765819772012613, 0.003127254657020615, 0.0001295130396491743, \
6.882352014430076e-14, 1.3821277371353332e-13, 1.3951939946082493e-13, \
1.381612071786285e-13, 5.0861109163441125e-17, 1.0722120295517027e-10, \
2.425145934791457e-6, 3.557919265806602e-6, 3.6669510385105265e-6, \
5.97473789679846e-11, 6.155412262223178e-11]
for p in range(len(orientationlist)):
r = np.array(orientationlist[p])
x = np.array(positionlist[p])
npt.assert_almost_equal(k.evaluate_kernel(x, y, r, v), kernelvalues[p])
def test_kernel_input():
""" Test the kernel for inputs of type Sphere, type int and for input None"""
sph = Sphere(1, 0, 0)
D33 = 1.0
D44 = 0.04
t = 1
k = EnhancementKernel(D33, D44, t, orientations=sph, force_recompute=True)
npt.assert_equal(k.get_lookup_table().shape, (1, 1, 7, 7, 7))
num_orientations = 2
k = EnhancementKernel(D33,
D44,
t,
orientations=num_orientations,
force_recompute=True)
npt.assert_equal(k.get_lookup_table().shape, (2, 2, 7, 7, 7))
k = EnhancementKernel(D33, D44, t, orientations=0, force_recompute=True)
npt.assert_equal(k.get_lookup_table().shape, (0, 0, 7, 7, 7))
def test_normalization():
""" Test the normalization routine applied after a convolution"""
# create kernel
D33 = 1.0
D44 = 0.04
t = 1
num_orientations = 5
k = EnhancementKernel(D33, D44, t, orientations=num_orientations, force_recompute=True)
# create a constant dataset
numorientations = k.get_orientations().shape[0]
spike = np.ones((7, 7, 7, numorientations), dtype=np.float64)
# convert dataset to SH
spike_sh = sf_to_sh(spike, k.get_sphere(), sh_order=8)
# convolve kernel with delta spike and apply normalization
csd_enh = convolve(spike_sh, k, sh_order=8, test_mode=True, normalize=True)
# convert dataset to DSF
csd_enh_dsf = sh_to_sf(csd_enh, k.get_sphere(), sh_order=8, basis_type=None)
# test if the normalization is performed correctly
npt.assert_almost_equal(np.amax(csd_enh_dsf), np.amax(spike))
def test_kernel_input():
""" Test the kernel for inputs of type Sphere, type int and for input None"""
sph = Sphere(1, 0, 0)
D33 = 1.0
D44 = 0.04
t = 1
k = EnhancementKernel(D33, D44, t, orientations=sph, force_recompute=True)
npt.assert_equal(k.get_lookup_table().shape, (1, 1, 7, 7, 7))
num_orientations = 2
k = EnhancementKernel(D33, D44, t, orientations=num_orientations, force_recompute=True)
npt.assert_equal(k.get_lookup_table().shape, (2, 2, 7, 7, 7))
k = EnhancementKernel(D33, D44, t, orientations=0, force_recompute=True)
npt.assert_equal(k.get_lookup_table().shape, (0, 0, 7, 7, 7))
rotated versions of the fiber propagation kernel :math:`P_t` [DuitsAndFranken_IJCV]_ rotated over a discrete set of orientations. See the `Contextual enhancement example <http://nipy.org/dipy/examples_built/contextual_enhancement.html>`_ for more details regarding the kernel. In order to ensure rotationally invariant processing, the discrete orientations are required to be equally distributed over a sphere. By default, a sphere with 100 directions is used obtained from electrostatic repulsion in DIPY. """ # Compute lookup table 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.
Inspired by [Rodrigues2010]_, a lookup-table is created, containing
rotated versions of the kernel :math:`P_t` sampled over a discrete set of
orientations. In order to ensure rotationally invariant processing, the
discrete orientations are required to be equally distributed over a sphere.
By default, a sphere with 100 directions is used.
"""
from dipy.denoise.enhancement_kernel import EnhancementKernel
from dipy.denoise.shift_twist_convolution import convolve
# Create lookup table
D33 = 1.0
D44 = 0.02
t = 1
k = EnhancementKernel(D33, D44, t)
"""
Visualize the kernel
"""
from dipy.viz import window, actor
from dipy.reconst.shm import sf_to_sh, sh_to_sf
scene = window.Scene()
# convolve kernel with delta spike
spike = np.zeros((7, 7, 7, k.get_orientations().shape[0]), dtype=np.float64)
spike[3, 3, 3, 0] = 1
spike_shm_conv = convolve(sf_to_sh(spike, k.get_sphere(), sh_order=8),
k,
sh_order=8,
test_mode=True)
Inspired by [RodriguesEurographics_], a lookup-table is created, containing
rotated versions of the kernel :math:`P_t` sampled over a discrete set of
orientations. In order to ensure rotationally invariant processing, the discrete
orientations are required to be equally distributed over a sphere. Per default,
a sphere with 100 directions is used.
"""
from dipy.denoise.enhancement_kernel import EnhancementKernel
from dipy.denoise.shift_twist_convolution import convolve
# Create lookup table
D33 = 1.0
D44 = 0.02
t = 1
k = EnhancementKernel(D33, D44, t)
"""
Visualize the kernel
"""
from dipy.viz import fvtk
from dipy.data import get_sphere
from dipy.reconst.shm import sf_to_sh, sh_to_sf
ren = fvtk.ren()
# convolve kernel with delta spike
spike = np.zeros((7, 7, 7, k.get_orientations().shape[0]), dtype=np.float64)
spike[3, 3, 3, 0] = 1
spike_shm_conv = convolve(sf_to_sh(spike, k.get_sphere(), sh_order=8), k,
sh_order=8, test_mode=True)