def extract_clusters_from_diam(T,XYZ,th,diam,k=18):
"""
Extract clusters from a statistical map
under diameter constraint
and above given threshold
In: T (p) statistical map
XYZ (3,p) voxels coordinates
th <float> minimum threshold
diam <int> maximal diameter (in voxels)
k <int> the number of neighbours considered. (6,18 or 26)
Out: labels (p) cluster labels
"""
CClabels = extract_clusters_from_thresh(T,XYZ,th,k)
nCC = CClabels.max() + 1
labels = -np.ones(len(CClabels),int)
clust_label = 0
for i in xrange(nCC):
#print "Searching connected component ", i, " out of ", nCC
I = np.where(CClabels==i)[0]
extCC = len(I)
if extCC <= (diam+1)**3:
diamCC = max_dist(XYZ,I,I)
else:
diamCC = diam+1
if diamCC <= diam:
labels[I] = np.zeros(extCC,int) + clust_label
#print "cluster ", clust_label, ", diam = ", diamCC
#print "ext = ", len(I), ", diam = ", max_dist(XYZ,I,I)
clust_label += 1
else:
# build the field
p = len(T[I])
F = Field(p)
F.from_3d_grid(np.transpose(XYZ[:,I]),k)
F.set_field(np.reshape(T[I],(p,1)))
# compute the blobs
idx, height, parent,label = F.threshold_bifurcations(0,th)
nidx = np.size(idx)
#root = nidx-1
root = np.where(np.arange(nidx)==parent)[0]
# Can constraint be met within current region?
Imin = I[T[I]>=height[root]]
extmin = len(Imin)
if extmin <= (diam+1)**3:
dmin = max_dist(XYZ,Imin,Imin)
else:
dmin = diam+1
if dmin <= diam:# If so, search for the largest cluster meeting the constraint
Iclust = Imin # Smallest cluster
J = I[T[I]<height[root]] # Remaining voxels
argsortTJ = np.argsort(T[J])[::-1] # Sorted by decreasing T values
l = 0
L = np.array([J[argsortTJ[l]]],int)
diameter = dmin
new_diameter = max(dmin,max_dist(XYZ,Iclust,L))
while new_diameter <= diam:
#print "diameter = " + str(new_diameter)
#sys.stdout.flush()
Iclust = np.concatenate((Iclust,L))
diameter = new_diameter
#print "diameter = ", diameter
l += 1
L = np.array([J[argsortTJ[l]]],int)
new_diameter = max(diameter,max_dist(XYZ,Iclust,L))
labels[Iclust] = np.zeros(len(Iclust),int) + clust_label
#print "cluster ", clust_label, ", diam = ", diameter
#print "ext = ", len(Iclust), ", diam = ", max_dist(XYZ,Iclust,Iclust)
clust_label += 1
else:# If not, search inside sub-regions
#print "Searching inside sub-regions "
Irest = I[T[I]>height[root]]
rest_labels = extract_clusters_from_diam(T[Irest],XYZ[:,Irest],th,diam,k)
rest_labels[rest_labels>=0] += clust_label
clust_label = rest_labels.max() + 1
labels[Irest] = rest_labels
return labels
""" print __doc__ import numpy as np import os from nipy.io.imageformats import load, save, Nifti1Image from nipy.neurospin.graph.field import Field import get_data_light import tempfile data_dir = get_data_light.get_it() # paths swd = tempfile.mkdtemp() input_image = os.path.join(data_dir, 'spmT_0029.nii.gz') mask_image = os.path.join(data_dir, 'mask.nii.gz') mask = load(mask_image).get_data()>0 ijk = np.array(np.where(mask)).T nvox = ijk.shape[0] data = load(input_image).get_data()[mask] image_field = Field(nvox) image_field.from_3d_grid(ijk, k=6) image_field.set_field(data) u = image_field.ward(100) label_image = os.path.join(swd, 'label.nii') wdata = mask - 1 wdata[mask] = u save(Nifti1Image(wdata, load(mask_image).get_affine()), label_image) print "Label image written in %s" % label_image
