def test_reduce_labels():
shape = (4, 5, 6)
# labels from 100 to 220
labels = np.arange(100, np.prod(shape) + 100).reshape(shape)
# new labels form 0 to 120, and lookup maps range(0,120) to range(100, 220)
new_labels, lookup = reduce_labels(labels)
npt.assert_array_equal(new_labels, labels - 100)
npt.assert_array_equal(lookup, labels.ravel())
def test_reduce_labels():
shape = (4, 5, 6)
# labels from 100 to 220
labels = np.arange(100, np.prod(shape)+100).reshape(shape)
# new labels form 0 to 120, and lookup maps range(0,120) to range(100, 220)
new_labels, lookup = reduce_labels(labels)
assert_array_equal(new_labels, labels-100)
assert_array_equal(lookup, labels.ravel())
def main():
parser = buildArgsParser()
args = parser.parse_args()
if not os.path.isfile(args.tracts):
parser.error("Tracts file: {0} does not exist.".format(args.tracts))
# TODO check scilpy supports
if not os.path.isfile(args.aparc):
parser.error("Label file: {0} does not exist.".format(args.aparc))
if not os.path.isfile(args.labels):
parser.error("Requested region file: {0} does not exist.".format(
args.labels))
if not os.path.isfile(args.lut):
parser.error("Freesurfer LUT file: {0} does not exist.".format(
args.lut))
if os.path.isfile(args.out_matrix) and not args.force_overwrite:
parser.error(
"Output: {0} already exists. To overwrite, use -f.".format(
args.out_matrix))
if os.path.isfile(args.out_row_map) and not args.force_overwrite:
parser.error(
"Output: {0} already exists. To overwrite, use -f.".format(
args.out_row_map))
if os.path.splitext(args.out_matrix)[1] != ".npy":
parser.error("Connectivity matrix must be saved in a .npy file.")
if os.path.splitext(args.out_row_map)[1] != ".pkl":
parser.error("Mapping must be saved in a .pkl file.")
# Validate that tracts can be processed
if not validate_coordinates(args.aparc, args.tracts, nifti_compliant=True):
parser.error("The tracts file contains points that are invalid.\n" +
"Use the remove_invalid_coordinates.py script to clean.")
# Load labels
labels_img = nib.load(args.aparc)
full_labels = labels_img.get_data().astype('int')
# Compute the mapping from label name to label id
label_id_mapping = compute_labels_map(args.lut)
# Find which labels were requested by the user.
requested_labels_mapping = compute_requested_labels(
args.labels, label_id_mapping)
# Filter to keep only needed ones
filtered_labels = np.zeros(full_labels.shape, dtype='int')
for label_val in requested_labels_mapping:
filtered_labels[full_labels == label_val] = label_val
# Reduce the range of labels to avoid a sparse matrix,
# because the ids of labels can range from 0 to the 12000's.
reduced_labels, labels_lut = dpu.reduce_labels(filtered_labels)
# Load tracts
tract_format = tc.detect_format(args.tracts)
tract = tract_format(args.tracts, args.aparc)
streamlines = [t for t in tract]
f_streamlines = []
for sl in streamlines:
# Avoid streamlines having only one point, as they crash the
# Dipy connectivity matrix function.
if sl.shape[0] > 1:
f_streamlines.append(sl)
# Compute affine
affine = compute_affine_for_dipy_functions(args.aparc, args.tracts)
# Compute matrix
M = dpu.connectivity_matrix(f_streamlines,
reduced_labels,
affine=affine,
symmetric=True,
return_mapping=False,
mapping_as_streamlines=False)
# Remove background connectivity
M = M[1:, 1:]
# Save needed files
np.save(args.out_matrix, np.array(M))
# Compute the mapping between row numbers, labels and ids.
sorted_lut = sorted(labels_lut)
row_name_map = {}
# Skip first for BG
for id, lab_val in enumerate(sorted_lut[1:]):
# Find the associated Freesurfer id
free_name = requested_labels_mapping[lab_val]['free_name']
lut_name = requested_labels_mapping[lab_val]['lut_name']
# Find the mean y position of the label to be able to spatially sort.
positions = np.where(full_labels == lab_val)
mean_y = np.mean(positions[1])
row_name_map[id] = {
'free_name': free_name,
'lut_name': lut_name,
'free_label': lab_val,
'mean_y_pos': mean_y
}
with open(args.out_row_map, 'w') as f:
pickle.dump(row_name_map, f)
def main():
parser = buildArgsParser()
args = parser.parse_args()
if not os.path.isfile(args.tracts):
parser.error("Tracts file: {0} does not exist.".format(args.tracts))
if not os.path.isfile(args.aparc):
parser.error("Label file: {0} does not exist.".format(args.aparc))
if not os.path.isfile(args.labels):
parser.error("Requested region file: {0} does not exist.".format(
args.labels))
if not os.path.isfile(args.lut):
parser.error("Freesurfer LUT file: {0} does not exist.".format(
args.lut))
if not os.path.isfile(args.faimage):
parser.error("FA Image file: {0} does not exist.".format(args.faimage))
if not os.path.isfile(args.mdimage):
parser.error("MD Image file: {0} does not exist.".format(args.mdimage))
# Validate that tracts can be processed
if not validate_coordinates(args.aparc, args.tracts, nifti_compliant=True):
parser.error("The tracts file contains points that are invalid.\n" +
"Use the remove_invalid_coordinates.py script to clean.")
# Load label image
labels_img = nib.load(args.aparc)
full_labels = labels_img.get_data().astype('int')
# Load fibers
tract_format = tc.detect_format(args.tracts)
tract = tract_format(args.tracts, args.aparc)
affine = compute_affine_for_dipy_functions(args.aparc, args.tracts)
#load FA and MD image
fa_img = nib.load(args.faimage)
fa_data = fa_img.get_data()
md_img = nib.load(args.mdimage)
md_data = md_img.get_data()
# ========= processing streamlines =================
fiberlen_range = np.asarray([args.minlen, args.maxlen])
streamlines = [t for t in tract]
print "Subject " + args.sub_id + " has " + str(
len(streamlines)) + " raw streamlines."
f_streamlines = [] #filtered streamlines
lenrecord = []
idx = 0
for sl in streamlines:
# Avoid streamlines having only one point, as they crash the
# Dipy connectivity matrix function.
if sl.shape[0] > 1:
flen = length(sl)
# get fibers having length between 20mm and 200mm
if (flen > fiberlen_range[0]) & (flen < fiberlen_range[1]):
f_streamlines.append(sl)
lenrecord.append(flen)
idx = idx + 1
print "Subject " + args.sub_id + " has " + str(
idx) + " streamlines with lengths between " + str(
args.minlen) + " and " + str(args.maxlen) + "."
# ============= process the parcellation =====================
dilation_para = np.array([args.dilation_dist, args.dilation_windsize])
# Compute the mapping from label name to label id
label_id_mapping = compute_labels_map(args.lut)
# Find which labels were requested by the user.
requested_labels_mapping = compute_requested_labels(
args.labels, label_id_mapping)
# Filter to keep only needed ones
filtered_labels = np.zeros(full_labels.shape, dtype='int')
for label_val in requested_labels_mapping:
if sum(sum(sum(full_labels == label_val))) == 0:
print label_val
print requested_labels_mapping[label_val]
filtered_labels[full_labels == label_val] = label_val
#cortex band dilation
dilated_labels = cortexband_dilation_wm(filtered_labels, full_labels,
dilation_para)
# Reduce the range of labels to avoid a sparse matrix,
# because the ids of labels can range from 0 to the 12000's.
reduced_labels, labels_lut = dpu.reduce_labels(filtered_labels)
reduced_dilated_labels, labels_lut = dpu.reduce_labels(dilated_labels)
# Compute connectivity matrix and extract the fibers
M, grouping = nconnectivity_matrix(f_streamlines,
reduced_dilated_labels,
fiberlen_range,
args.cnpoint,
affine=affine,
symmetric=True,
return_mapping=True,
mapping_as_streamlines=True)
Msize = len(M)
CM_before_outlierremove = M[1:, 1:]
nstream_bf = np.sum(CM_before_outlierremove)
print args.sub_id + ' ' + str(
nstream_bf
) + ' streamlines in the connectivity matrix before outlier removal.'
#===================== process the streamlines =============
print 'Processing streamlines to remove outliers ..............'
outlier_para = 3
average_thrd = 8
M_after_ourlierremove = np.zeros((Msize, Msize))
#downsample streamlines
cell_streamlines = []
cell_id = []
for i in range(1, Msize):
for j in range(i + 1, Msize):
tmp_streamlines = grouping[i, j]
tmp_streamlines = list(tmp_streamlines)
#downsample
tmp_streamlines_downsampled = [
downsample(s, 100) for s in tmp_streamlines
]
#remove outliers, we need to rewrite the QuickBundle method to speed up this process
qb = QuickBundles(threshold=average_thrd)
clusters = qb.cluster(tmp_streamlines_downsampled)
outlier_clusters = clusters < outlier_para #small clusters
nonoutlier_clusters = clusters[np.logical_not(outlier_clusters)]
tmp_nonoutlier_index = []
for tmp_cluster in nonoutlier_clusters:
tmp_nonoutlier_index = tmp_nonoutlier_index + tmp_cluster.indices
clean_streamline_downsampled = [
tmp_streamlines_downsampled[ind]
for ind in tmp_nonoutlier_index
]
cell_streamlines.append(clean_streamline_downsampled)
cell_id.append([i, j])
M_after_ourlierremove[i, j] = len(clean_streamline_downsampled)
CM_after_ourlierremove = M_after_ourlierremove[1:, 1:]
nstream_bf = np.sum(CM_after_ourlierremove)
print args.sub_id + ' ' + str(
nstream_bf
) + ' streamlines in the connectivity matrix after outlier removal.'
#save streamlines and count matrix
cmCountMatrix_fname = args.sub_id + "_" + args.pre + "_cm_count_raw.mat"
cmCountMatrix_processed_fname = args.sub_id + "_" + args.pre + "_cm_count_processed.mat"
cmStreamlineMatrix_fname = args.sub_id + "_" + args.pre + "_cm_streamlines.mat"
reduced_labels_fname = args.sub_id + "_" + args.pre + "_reduced_labels.nii.gz"
dilated_labels_fname = args.sub_id + "_" + args.pre + "_dilated_labels.nii.gz"
RoiInfo_fname = args.sub_id + "_" + args.pre + "_RoiInfo.mat"
# save the raw count matrix
CM = M[1:, 1:]
sio.savemat(cmCountMatrix_fname, {'cm': CM})
sio.savemat(cmCountMatrix_processed_fname, {'cm': CM_after_ourlierremove})
# save the streamline matrix
sio.savemat(cmStreamlineMatrix_fname, {'slines': cell_streamlines})
sio.savemat(RoiInfo_fname, {'ROIinfo': cell_id})
print args.sub_id + 'cell_streamlines.mat, ROIinfo.mat has been saved'
filtered_labels_img = nib.Nifti1Image(filtered_labels,
labels_img.get_affine(),
labels_img.get_header())
nib.save(filtered_labels_img, reduced_labels_fname)
print args.sub_id + 'filtered labels have saved'
dilated_labels_img = nib.Nifti1Image(dilated_labels,
labels_img.get_affine(),
labels_img.get_header())
nib.save(dilated_labels_img, dilated_labels_fname)
print args.sub_id + 'dilated labels have saved'
# ===================== process the streamlines and extract features =============
cm_fa_curve = fa_extraction_use_cellinput(cell_streamlines,
cell_id,
fa_data,
Msize,
affine=affine)
(tmp_cm_fa_mean, tmp_cm_fa_max,
cm_count) = fa_mean_extraction(cm_fa_curve, Msize)
# extract MD values along the streamlines
cm_md_curve = fa_extraction_use_cellinput(cell_streamlines,
cell_id,
md_data,
Msize,
affine=affine)
(tmp_cm_md_mean, tmp_cm_md_max,
testcm) = fa_mean_extraction(cm_md_curve, Msize)
#connected surface area
# extract the connective volume ratio
(tmp_cm_volumn,
tmp_cm_volumn_ratio) = rois_connectedvol_cellinput(reduced_labels,
Msize,
cell_streamlines,
cell_id,
affine=affine)
#fiber length
tmp_connectcm_len = rois_fiberlen_cellinput(Msize, cell_streamlines)
#save cm features
cm_md_mean = tmp_cm_md_mean[1:, 1:]
cm_md_max = tmp_cm_md_max[1:, 1:]
cm_fa_mean = tmp_cm_fa_mean[1:, 1:]
cm_fa_max = tmp_cm_fa_max[1:, 1:]
cm_volumn = tmp_cm_volumn[1:, 1:]
cm_volumn_ratio = tmp_cm_volumn_ratio[1:, 1:]
connectcm_len = tmp_connectcm_len[1:, 1:]
sio.savemat(args.pre + "_cm_processed_mdmean_100.mat",
{'cm_mdmean': cm_md_mean})
sio.savemat(args.pre + "_cm_processed_mdmax_100.mat",
{'cm_mdmax': cm_md_max})
sio.savemat(args.pre + "_cm_processed_famean_100.mat",
{'cm_famean': cm_fa_mean})
sio.savemat(args.pre + "_cm_processed_famax_100.mat",
{'cm_famax': cm_fa_max})
sio.savemat(args.pre + "_cm_processed_volumn_100.mat",
{'cm_volumn': cm_volumn})
sio.savemat(args.pre + "_cm_processed_volumn_ratio_100.mat",
{'cm_volumn_ratio': cm_volumn_ratio})
sio.savemat(args.pre + "_cm_processed_volumn_ratio_100.mat",
{'cm_len': connectcm_len})
# save the diffusion functions matrix
cell_fa = []
for i in range(1, Msize):
for j in range(i + 1, Msize):
tmp_fa = cm_fa_curve[i, j]
tmp_fa = list(tmp_fa)
cell_fa.append(tmp_fa)
sio.savemat(args.pre + "_cm_processed_sfa_100.mat", {'sfa': cell_fa})
print 'cell_fa.mat, fa_roiinfo.mat have been saved'
cell_md = []
for i in range(1, Msize):
for j in range(i + 1, Msize):
tmp_md = cm_md_curve[i, j]
tmp_md = list(tmp_md)
cell_md.append(tmp_md)
sio.savemat(args.pre + "_cm_processed_smd_100.mat", {'smd': cell_md})
def main():
parser = buildArgsParser()
args = parser.parse_args()
if not os.path.isfile(args.tracts):
parser.error("Tracts file: {0} does not exist.".format(args.tracts))
if not os.path.isfile(args.org_aparc):
parser.error("Original label file: {0} does not exist.".format(
args.org_aparc))
if not os.path.isfile(args.dilated_aparc):
parser.error("Dilated label file: {0} does not exist.".format(
args.dilated_aparc))
if not os.path.isfile(args.subcortical_labels):
parser.error("Requested region file: {0} does not exist.".format(
args.subcortical_labels))
if not os.path.isfile(args.lut):
parser.error("Freesurfer LUT file: {0} does not exist.".format(
args.lut))
if not os.path.isfile(args.faimage):
parser.error("FA Image file: {0} does not exist.".format(args.faimage))
if not os.path.isfile(args.mdimage):
parser.error("MD Image file: {0} does not exist.".format(args.mdimage))
# Validate that tracts can be processed
if not validate_coordinates(
args.org_aparc, args.tracts, nifti_compliant=True):
parser.error("The tracts file contains points that are invalid.\n" +
"Use the remove_invalid_coordinates.py script to clean.")
# Load label images
org_labels_img = nib.load(args.org_aparc)
org_labels_data = org_labels_img.get_data().astype('int')
dilated_labels_img = nib.load(args.dilated_aparc)
dilated_labels_data = dilated_labels_img.get_data().astype('int')
# Load fibers
tract_format = tc.detect_format(args.tracts)
tract = tract_format(args.tracts, args.org_aparc)
affine = compute_affine_for_dipy_functions(args.org_aparc, args.tracts)
#load FA and MD image
fa_img = nib.load(args.faimage)
fa_data = fa_img.get_data()
md_img = nib.load(args.mdimage)
md_data = md_img.get_data()
# ========= processing streamlines =================
fiberlen_range = np.asarray([args.minlen, args.maxlen])
streamlines = [t for t in tract]
print "Subjeect " + args.sub_id + " has " + str(
len(streamlines)) + " streamlines."
f_streamlines = [] #filtered streamlines
lenrecord = []
idx = 0
for sl in streamlines:
# Avoid streamlines having only one point, as they crash the
# Dipy connectivity matrix function.
if sl.shape[0] > 1:
flen = length(sl)
# get fibers having length between 20mm and 200mm
if (flen > fiberlen_range[0]) & (flen < fiberlen_range[1]):
f_streamlines.append(sl)
lenrecord.append(flen)
idx = idx + 1
print "Subject " + args.sub_id + " has " + str(
idx - 1) + " streamlines after filtering."
# ============= process the parcellation =====================
# Compute the mapping from label name to label id
label_id_mapping = compute_labels_map(args.lut)
# Find which labels were requested by the user.
requested_labels_mapping = compute_requested_labels(
args.subcortical_labels, label_id_mapping)
# Increase aparc_filtered_labels with subcortical regions
# 17 LH_Hippocampus
# 53 RH_Hippocampus
# 11 LH_Caudate
# 50 RH_Caudate
# 12 LH_Putamen
# 51 RH_Putamen
# 13 LH_Pallidum
# 52 RH_Pallidum
# 18 LH_Amygdala
# 54 RH_Amygdala
# 26 LH_Accumbens
# 58 RH_Accumbens
# 10 LH_Thalamus-Proper
# 49 RH_Thalamus-Proper
# 4 LH_Lateral-Ventricle
# 43 RH_Lateral-Ventricle
# 8 LH_Cerebellum-Cortex
# 47 RH_Cerebellum-Cortex
#
# 16 _Brain-Stem (# 7,8 LH_Cerebellum) (# 41 RH_Cerebellum)
sub_cortical_labels = [
17, 53, 11, 50, 12, 51, 13, 52, 18, 54, 26, 58, 10, 49, 4, 43, 8, 47
] # 16
Brain_Stem_cerebellum = [16] #1
aparc_filtered_labels = dilated_labels_data
for label_val in requested_labels_mapping:
if sum(sum(sum(org_labels_data == label_val))) == 0:
print label_val
print requested_labels_mapping[label_val]
aparc_filtered_labels[org_labels_data == label_val] = label_val
for brain_stem_id in Brain_Stem_cerebellum:
if sum(sum(sum(org_labels_data == brain_stem_id))) == 0:
print 'no labels of '
print brain_stem_id
aparc_filtered_labels[
org_labels_data ==
brain_stem_id] = 99 # let the brain stem's label be 30
# Reduce the range of labels to avoid a sparse matrix,
# because the ids of labels can range from 0 to the 12000's.
reduced_dilated_labels, labels_lut = dpu.reduce_labels(
aparc_filtered_labels)
#dilated_labels_fname = args.sub_id + "_" + args.pre + "_dilated_allbrain_labels.nii.gz"
#dilated_labels_img = nib.Nifti1Image(aparc_filtered_labels, org_labels_img.get_affine(),org_labels_img.get_header())
#nib.save(dilated_labels_img,dilated_labels_fname)
#print args.sub_id + 'dilated labels have saved'
#pdb.set_trace()
# Compute connectivity matrix and extract the fibers
M, grouping = nconnectivity_matrix(f_streamlines,
reduced_dilated_labels,
fiberlen_range,
args.cnpoint,
affine=affine,
symmetric=True,
return_mapping=True,
mapping_as_streamlines=True,
keepfiberinroi=True)
Msize = len(M)
CM_before_outlierremove = M[1:, 1:]
nstream_bf = np.sum(CM_before_outlierremove)
print args.sub_id + ' ' + str(
nstream_bf
) + ' streamlines in the connectivity matrix before outlier removal.'
# ===================== process the streamlines =============
print 'Processing streamlines to remove outliers ..............'
outlier_para = 3
average_thrd = 8
M_after_ourlierremove = np.zeros((Msize, Msize))
# downsample streamlines
cell_streamlines = []
cell_id = []
for i in range(1, Msize):
for j in range(i + 1, Msize):
tmp_streamlines = grouping[i, j]
tmp_streamlines = list(tmp_streamlines)
# downsample
tmp_streamlines_downsampled = [
downsample(s, 100) for s in tmp_streamlines
]
# remove outliers, we need to rewrite the QuickBundle method to speed up this process
qb = QuickBundles(threshold=average_thrd)
clusters = qb.cluster(tmp_streamlines_downsampled)
outlier_clusters = clusters < outlier_para # small clusters
nonoutlier_clusters = clusters[np.logical_not(outlier_clusters)]
tmp_nonoutlier_index = []
for tmp_cluster in nonoutlier_clusters:
tmp_nonoutlier_index = tmp_nonoutlier_index + tmp_cluster.indices
clean_streamline_downsampled = [
tmp_streamlines_downsampled[ind]
for ind in tmp_nonoutlier_index
]
cell_streamlines.append(clean_streamline_downsampled)
cell_id.append([i, j])
M_after_ourlierremove[i, j] = len(clean_streamline_downsampled)
CM_after_ourlierremove = M_after_ourlierremove[1:, 1:]
nstream_bf = np.sum(CM_after_ourlierremove)
print args.sub_id + ' ' + str(
nstream_bf
) + ' streamlines in the connectivity matrix after outlier removal.'
#===================== save the data =======================
if (args.saving_indicator == 1): # save the whole brain connectivity
cmCountMatrix_fname = args.sub_id + "_" + args.pre + "_allbrain" + "_cm_count_raw.mat"
cmCountMatrix_processed_fname = args.sub_id + "_" + args.pre + "_allbrain" + "_cm_count_processed.mat"
cmStreamlineMatrix_fname = args.sub_id + "_" + args.pre + "_allbrain" + "_cm_streamlines.mat"
reduced_dilated_labels_fname = args.sub_id + "_" + args.pre + "_allbrain" + "_reduced_dilated_labels.nii.gz"
RoiInfo_fname = args.sub_id + "_" + args.pre + "_allbrain_RoiInfo.mat"
# save the raw count matrix
CM = M[1:, 1:]
sio.savemat(cmCountMatrix_fname, {'cm': CM})
sio.savemat(cmCountMatrix_processed_fname,
{'cm': CM_after_ourlierremove})
# save the streamline matrix
sio.savemat(cmStreamlineMatrix_fname, {'slines': cell_streamlines})
sio.savemat(RoiInfo_fname, {'ROIinfo': cell_id})
print args.sub_id + 'cell_streamlines.mat, ROIinfo.mat has been saved'
filtered_labels_img = nib.Nifti1Image(aparc_filtered_labels,
org_labels_img.get_affine(),
org_labels_img.get_header())
nib.save(filtered_labels_img, reduced_dilated_labels_fname)
print args.sub_id + 'all brain dilated labels have saved'
# ===================== process the streamlines and extract features =============
cm_fa_curve = fa_extraction_use_cellinput(cell_streamlines,
cell_id,
fa_data,
Msize,
affine=affine)
(tmp_cm_fa_mean, tmp_cm_fa_max,
cm_count) = fa_mean_extraction(cm_fa_curve, Msize)
# extract MD values along the streamlines
cm_md_curve = fa_extraction_use_cellinput(cell_streamlines,
cell_id,
md_data,
Msize,
affine=affine)
(tmp_cm_md_mean, tmp_cm_md_max,
testcm) = fa_mean_extraction(cm_md_curve, Msize)
# connected surface area
# extract the connective volume ratio
(tmp_cm_volumn, tmp_cm_volumn_ratio) = rois_connectedvol_cellinput(
reduced_dilated_labels,
Msize,
cell_streamlines,
cell_id,
affine=affine)
# fiber length
tmp_connectcm_len = rois_fiberlen_cellinput(Msize, cell_streamlines)
# save cm features
cm_md_mean = tmp_cm_md_mean[1:, 1:]
cm_md_max = tmp_cm_md_max[1:, 1:]
cm_fa_mean = tmp_cm_fa_mean[1:, 1:]
cm_fa_max = tmp_cm_fa_max[1:, 1:]
cm_volumn = tmp_cm_volumn[1:, 1:]
cm_volumn_ratio = tmp_cm_volumn_ratio[1:, 1:]
connectcm_len = tmp_connectcm_len[1:, 1:]
sio.savemat(args.pre + "_allbrain" + "_cm_processed_mdmean_100.mat",
{'cm_mdmean': cm_md_mean})
sio.savemat(args.pre + "_allbrain" + "_cm_processed_mdmax_100.mat",
{'cm_mdmax': cm_md_max})
sio.savemat(args.pre + "_allbrain" + "_cm_processed_famean_100.mat",
{'cm_famean': cm_fa_mean})
sio.savemat(args.pre + "_allbrain" + "_cm_processed_famax_100.mat",
{'cm_famax': cm_fa_max})
sio.savemat(args.pre + "_allbrain" + "_cm_processed_volumn_100.mat",
{'cm_volumn': cm_volumn})
sio.savemat(
args.pre + "_allbrain" + "_cm_processed_volumn_ratio_100.mat",
{'cm_volumn_ratio': cm_volumn_ratio})
sio.savemat(args.pre + "_allbrain" + "_cm_processed_fiberlen_100.mat",
{'cm_len': connectcm_len})
# save the streamline matrix
cell_fa = []
for i in range(1, Msize):
for j in range(i + 1, Msize):
tmp_fa = cm_fa_curve[i, j]
tmp_fa = list(tmp_fa)
cell_fa.append(tmp_fa)
sio.savemat(args.pre + "_allbrain" + "_cm_processed_sfa_100.mat",
{'sfa': cell_fa})
print args.pre + '_allbrain" + "_cm_processed_sfa_100.mat' + ' has been saved'
cell_md = []
for i in range(1, Msize):
for j in range(i + 1, Msize):
tmp_md = cm_md_curve[i, j]
tmp_md = list(tmp_md)
cell_md.append(tmp_md)
sio.savemat(args.pre + "_allbrain" + "_cm_processed_smd_100.mat",
{'smd': cell_md})
print args.pre + '_allbrain" + "_cm_processed_smd_100.mat' + ' has been saved'
if (
args.saving_indicator == 0
): # save the part of the connection: connection between subcortical region
Nsubcortical_reg = len(sub_cortical_labels) + 1 # should be 19
cmCountMatrix_fname = args.sub_id + "_" + args.pre + "_partbrain_subcort" + "_cm_count_raw.mat"
cmCountMatrix_processed_fname = args.sub_id + "_" + args.pre + "_partbrain_subcort" + "_cm_count_processed.mat"
cmStreamlineMatrix_fname = args.sub_id + "_" + args.pre + "_partbrain_subcort" + "_cm_streamlines.mat"
reduced_dilated_labels_fname = args.sub_id + "_" + args.pre + "_partbrain_subcort" + "_reduced_dilated_labels.nii.gz"
subcortical_RoiInfo_fname = args.sub_id + "_" + args.pre + "_partbrain_subcort_RoiInfo.mat"
# save the raw count matrix
CM = M[1:, 1:]
sio.savemat(cmCountMatrix_fname, {'cm': CM})
sio.savemat(cmCountMatrix_processed_fname,
{'cm': CM_after_ourlierremove})
filtered_labels_img = nib.Nifti1Image(aparc_filtered_labels,
org_labels_img.get_affine(),
org_labels_img.get_header())
nib.save(filtered_labels_img, reduced_dilated_labels_fname)
print args.sub_id + ' all brain dilated labels have saved'
# ===================== process the streamlines and extract features =============
cm_fa_curve = fa_extraction_use_cellinput(cell_streamlines,
cell_id,
fa_data,
Msize,
affine=affine)
(tmp_cm_fa_mean, tmp_cm_fa_max,
cm_count) = fa_mean_extraction(cm_fa_curve, Msize)
# extract MD values along the streamlines
cm_md_curve = fa_extraction_use_cellinput(cell_streamlines,
cell_id,
md_data,
Msize,
affine=affine)
(tmp_cm_md_mean, tmp_cm_md_max,
testcm) = fa_mean_extraction(cm_md_curve, Msize)
# connected surface area
# extract the connective volume ratio
(tmp_cm_volumn, tmp_cm_volumn_ratio) = rois_connectedvol_cellinput(
reduced_dilated_labels,
Msize,
cell_streamlines,
cell_id,
affine=affine)
# fiber length
tmp_connectcm_len = rois_fiberlen_cellinput(Msize, cell_streamlines)
# save cm features
cm_md_mean = tmp_cm_md_mean[1:, 1:]
cm_md_max = tmp_cm_md_max[1:, 1:]
cm_fa_mean = tmp_cm_fa_mean[1:, 1:]
cm_fa_max = tmp_cm_fa_max[1:, 1:]
cm_volumn = tmp_cm_volumn[1:, 1:]
cm_volumn_ratio = tmp_cm_volumn_ratio[1:, 1:]
connectcm_len = tmp_connectcm_len[1:, 1:]
sio.savemat(
args.pre + "_partbrain_subcort" + "_cm_processed_mdmean_100.mat",
{'cm_mdmean': cm_md_mean})
sio.savemat(
args.pre + "_partbrain_subcort" + "_cm_processed_mdmax_100.mat",
{'cm_mdmax': cm_md_max})
sio.savemat(
args.pre + "_partbrain_subcort" + "_cm_processed_famean_100.mat",
{'cm_famean': cm_fa_mean})
sio.savemat(
args.pre + "_partbrain_subcort" + "_cm_processed_famax_100.mat",
{'cm_famax': cm_fa_max})
sio.savemat(
args.pre + "_partbrain_subcort" + "_cm_processed_volumn_100.mat",
{'cm_volumn': cm_volumn})
sio.savemat(
args.pre + "_partbrain_subcort" +
"_cm_processed_volumn_ratio_100.mat",
{'cm_volumn_ratio': cm_volumn_ratio})
sio.savemat(
args.pre + "_partbrain_subcort" + "_cm_processed_fiberlen_100.mat",
{'cm_len': connectcm_len})
# save the streamline matrix
cell_fa = []
cell_id = []
for i in range(1, Nsubcortical_reg):
for j in range(i + 1, Msize):
tmp_fa = cm_fa_curve[i, j]
tmp_fa = list(tmp_fa)
cell_fa.append(tmp_fa)
cell_id.append([i, j])
sio.savemat(
args.pre + "_partbrain_subcort" + "_cm_processed_sfa_100.mat",
{'sfa': cell_fa})
print args.pre + '_partbrain_subcort' + '_cm_processed_sfa_100.mat' + 'has been saved.'
cell_md = []
for i in range(1, Nsubcortical_reg):
for j in range(i + 1, Msize):
tmp_md = cm_md_curve[i, j]
tmp_md = list(tmp_md)
cell_md.append(tmp_md)
sio.savemat(args.pre + "_partbrain" + "_cm_processed_smd_100.mat",
{'smd': cell_md})
print args.pre + '_partbrain' + '_cm_processed_smd_100.mat' + 'has been saved.'
# save the streamline matrix
subcortical_cell_streamlines = []
cell_id = []
idx = 0
for i in range(1, Nsubcortical_reg):
for j in range(i + 1, Msize):
tmp_sls = cell_streamlines[idx]
idx = idx + 1
subcortical_cell_streamlines.append(tmp_sls)
cell_id.append([i, j])
sio.savemat(cmStreamlineMatrix_fname,
{'slines': subcortical_cell_streamlines})
sio.savemat(subcortical_RoiInfo_fname, {'ROIinfo': cell_id})
print cmStreamlineMatrix_fname + ' has been saved'
