def discrete_to_roi_features(self, fid, method='average'):
"""
Compute an ROI-level feature given the discrete features
Parameters
----------
fid(string) the discrete feature under consideration
method='average' the assessment method
Results
------
the computed roi-feature is returned
"""
if method not in['min','max','average','cumulated_average']:
raise ValueError, 'unknown method'
if method=='cumulated_average':
df = self.discrete_features[fid]
data = self.discrete_features[fid]
d0 = data[0]
if np.size(d0) == np.shape(d0)[0]:
np.reshape(d0,(np.size(d0),1))
fdim = d0.shape[1]
ldata = np.zeros((self.k,fdim))
for k in range(self.k):
dk = self.get_descendents(k)
card = np.sum(self.get_size()[dk])
for ch in dk:
ldata[k] += np.sum(data[ch],0)
ldata[k]/=card
self.set_roi_feature(fid,ldata)
else:
ldata = MultipleROI.discrete_to_roi_features(self, fid, method)
return ldata
def clean(self, valid):
"""
remove the rois for which valid==0
and update the hierarchy accordingly
In case sum(valid)==0, 0 is returned
"""
if np.sum(valid)==0:
return None
# fixme: is None a correct way of dealing with k=0 ?
# first clean as a forest
sf = self.subforest(valid)
Forest.__init__(self, sf.V, sf.parents)
# then clean as a multiple ROI
MultipleROI.clean(self, valid)
return self.V
def test_mroi2(verbose=0):
nim = load(RefImage)
mroi = MultipleROI(affine=nim.get_affine(), shape=nim.get_shape())
pos = 1.0*np.array([[10,10,10],[0,0,0],[20,0,20],[0,0,35]])
rad = np.array([5.,6.,7.,8.0])
mroi.as_multiple_balls(pos,rad)
mroi.append_balls(np.array([[-10.,0.,10.]]),np.array([7.0]))
mroi.set_roi_feature_from_image('T1_signal',RefImage)
avt1 = mroi.get_roi_feature('T1_signal')
assert(np.size(avt1)==5)
def test_mroi1(verbose=0):
nim = load(RefImage)
mroi = MultipleROI(affine=nim.get_affine(), shape=nim.get_shape())
pos = 1.0*np.array([[10,10,10],[0,0,0],[20,0,20],[0,0,35]])
rad = np.array([5.,6.,7.,8.0])
mroi.as_multiple_balls(pos,rad)
mroi.append_balls(np.array([[-10.,0.,10.]]),np.array([7.0]))
roiPath = os.path.join(WriteDir,"mroi.nii")
mroi.make_image(roiPath)
assert(os.path.isfile(roiPath))
def __init__(self, parents=None, affine=np.eye(4), shape=None,
xyz=None, id='nroi'):
"""
Building the NROI
Parameters
----------
parents=None: array of shape(k) providing
the hierachical structure
if parent==None, None is returned
affine=np.eye(4), array of shape(4,4),
coordinate-defining affine transformation
shape=None, tuple of length 3 defining the size of the grid
implicit to the discrete ROI definition
xyz=None, list of arrays of shape (3, nvox)
that yield the grid position of each grid guy.
id='nroi', string, region identifier
"""
if parents==None:
return None
k = np.size(parents)
Forest.__init__(self,k,parents)
MultipleROI.__init__(self,id, k, affine, shape, xyz)
save(wim, blobPath)
# --- 2.b take blob labelled "1" as an ROI
roi = DiscreteROI( affine=affine, shape=shape)
roi.from_labelled_image(blobPath, 1)
roiPath2 = os.path.join(swd, "roi_blob_1.nii")
roi.make_image(roiPath2)
# --- 2.c take the blob closest to 'position as an ROI'
roiPath3 = os.path.join(swd, "blob_closest_to_%d_%d_%d.nii")%\
(position[0], position[1], position[2])
roi.from_position_and_image(blobPath, np.array(position))
roi.make_image(roiPath3)
# --- 2.d make a set of ROIs from all the blobs
mroi = MultipleROI( affine=affine, shape=shape)
mroi.from_labelled_image(blobPath)
roiPath4 = os.path.join(swd, "roi_all_blobs.nii")
mroi.make_image(roiPath4)
mroi.set_discrete_feature_from_image('activ', InputImage)
mroi.discrete_to_roi_features('activ')
mroi.plot_roi_feature('activ')
# ---- 2.e the same, a bit more complex
mroi = MultipleROI( affine=affine, shape=shape)
mroi.as_multiple_balls(np.array([[-10.,0.,10.]]),np.array([7.0]))
mroi.from_labelled_image(blobPath,np.arange(1,20))
mroi.from_labelled_image(blobPath,np.arange(31,50))
roiPath5 = os.path.join(swd,"roi_some_blobs.nii")
mroi.set_discrete_feature_from_image('activ',InputImage)
mroi.discrete_to_roi_features('activ')
