def qb_metrics_features(streamlines,
threshold=10.0,
metric=None,
max_nb_clusters=np.iinfo('i4').max):
"""
Enhancing QuickBundles with different metrics and features
metric: 'IF', 'RF', 'CoMF', 'MF', 'AF', 'VBEF', None
"""
if metric == 'IF':
feature = IdentityFeature()
metric = AveragePointwiseEuclideanMetric(feature=feature)
elif metric == 'RF':
feature = ResampleFeature(nb_point=24)
metric = AveragePointwiseEuclideanMetric(feature=feature)
elif metric == 'CoMF':
feature = CenterOfMassFeature()
metric = EuclideanMetric(feature)
elif metric == 'MF':
feature = MidpointFeature()
metric = EuclideanMetric(feature)
elif metric == 'AF':
feature = ArcLengthFeature()
metric = EuclideanMetric(feature)
elif metric == 'VBEF':
feature = VectorOfEndpointsFeature()
metric = CosineMetric(feature)
else:
metric = "MDF_12points"
qb = QuickBundles(threshold=threshold,
metric=metric,
max_nb_clusters=max_nb_clusters)
clusters = qb.cluster(streamlines)
labels = np.array(len(streamlines) * [None])
N_list = []
for i in range(len(clusters)):
N_list.append(clusters[i]['N'])
data_clusters = []
for i in range(len(clusters)):
labels[clusters[i]['indices']] = i + 1
data_clusters.append(streamlines[clusters[i]['indices']])
return labels, data_clusters, N_list
**Note:** Since streamlines endpoints are ambiguous (e.g. the first point could
be either the beginning or the end of the streamline), one must be careful when
using this feature.
"""
import numpy as np
from dipy.viz import window, colormap
from dipy.segment.clustering import QuickBundles
from dipy.segment.metric import VectorOfEndpointsFeature
from dipy.segment.metric import CosineMetric
# 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))
def __init__(self):
# For simplicity, features will be the vector between endpoints of a streamline.
super(CosineMetric, self).__init__(feature=VectorOfEndpointsFeature())