This page lists available metrics that can be used by the tractography
clustering framework. For every metric a brief description is provided
explaining: what it does, when it’s useful and how to use it. If you are not
familiar with the tractography clustering framework, check this tutorial
Clustering framework. Note:
All examples assume a function get_streamlines exists. We defined here a
simple function to do so. It imports the necessary modules and loads a small
streamline bundle. What: Instances of AveragePointwiseEuclideanMetric first compute the
pointwise Euclidean distance between two sequences of same length then
return the average of those distances. This metric takes as inputs two features
that are sequences containing the same number of elements. When: By default the QuickBundles clustering will resample your
streamlines on-the-fly so they have 12 points. If for some reason you want
to avoid this and you made sure all your streamlines already have the same
number of points, you can manually provide an instance of
AveragePointwiseEuclideanMetric to QuickBundles. Since the default
Feature is the IdentityFeature the streamlines won’t be resampled thus
saving some computational time. Note: Inputs must be sequences of the same length. What: Instances of SumPointwiseEuclideanMetric first compute the
pointwise Euclidean distance between two sequences of same length then
return the sum of those distances. When: This metric mainly exists because it is used internally by
AveragePointwiseEuclideanMetric. Note: Inputs must be sequences of the same length. What: Instances of CosineMetric compute the cosine distance between two
vectors (for more information see the
wiki page). When: This metric can be useful when you only need information about the
orientation of a streamline. Note: Inputs must be vectors (i.e. 1D array). Garyfallidis E. et al., QuickBundles a method for
tractography simplification, Frontiers in Neuroscience, vol 6, no 175, 2012. Example source code You can download Tractography Clustering - Available Metrics
def get_streamlines():
from dipy.data import get_fnames
from dipy.io.streamline import load_tractogram
fname = get_fnames('fornix')
fornix = load_tractogram(fname, 'same', bbox_valid_check=False)
return fornix.streamlines
Average of Pointwise Euclidean Metric
from dipy.segment.clustering import QuickBundles
from dipy.segment.metric import AveragePointwiseEuclideanMetric
# Get some streamlines.
streamlines = get_streamlines() # Previously defined.
# Make sure our streamlines have the same number of points.
from dipy.tracking.streamline import set_number_of_points
streamlines = set_number_of_points(streamlines, nb_points=12)
# Create the instance of `AveragePointwiseEuclideanMetric` to use.
metric = AveragePointwiseEuclideanMetric()
qb = QuickBundles(threshold=10., metric=metric)
clusters = qb.cluster(streamlines)
print("Nb. clusters:", len(clusters))
print("Cluster sizes:", map(len, clusters))
Nb. clusters: 4
Cluster sizes: [64, 191, 44, 1]
Sum of Pointwise Euclidean Metric
from dipy.segment.clustering import QuickBundles
from dipy.segment.metric import SumPointwiseEuclideanMetric
# Get some streamlines.
streamlines = get_streamlines() # Previously defined.
# Make sure our streamlines have the same number of points.
from dipy.tracking.streamline import set_number_of_points
nb_points = 12
streamlines = set_number_of_points(streamlines, nb_points=nb_points)
# Create the instance of `SumPointwiseEuclideanMetric` to use.
metric = SumPointwiseEuclideanMetric()
qb = QuickBundles(threshold=10.*nb_points, metric=metric)
clusters = qb.cluster(streamlines)
print("Nb. clusters:", len(clusters))
print("Cluster sizes:", map(len, clusters))
Nb. clusters: 4
Cluster sizes: [64, 191, 44, 1]
Cosine Metric
import numpy as np
from dipy.viz import window, actor, colormap
from dipy.segment.clustering import QuickBundles
from dipy.segment.featurespeed import VectorOfEndpointsFeature
from dipy.segment.metric import CosineMetric
# Enables/disables interactive visualization
interactive = False
# Get some streamlines.
streamlines = get_streamlines() # Previously defined.
feature = VectorOfEndpointsFeature()
metric = CosineMetric(feature)
qb = QuickBundles(threshold=0.1, metric=metric)
clusters = qb.cluster(streamlines)
# Color each streamline according to the cluster they belong to.
colormap = colormap.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
colormap_full[cluster.indices] = color
# Visualization
scene = window.Scene()
scene.clear()
scene.SetBackground(0, 0, 0)
scene.add(actor.streamtube(streamlines, colormap_full))
window.record(scene, out_path='cosine_metric.png', size=(600, 600))
if interactive:
window.show(scene)
References
the full source code of this example
. This same script is also included in the dipy source distribution under the doc/examples/
directory.