def intensity_propagation(image, mask=None, combine='mean', distance_mm=5.0,
target='zero', scaling=1.0, domain=None,
save_data=False, overwrite=False, output_dir=None,
file_name=None):
""" Intensity Propogation
Propagates the values inside the mask (or non-zero) into the neighboring voxels
Parameters
----------
image: niimg
Input image
mask: niimg, optional
Data mask to specify acceptable seeding regions
combine: {'min','mean','max'}, optional
Propagate using the mean (default), max or min data from neighboring voxels
distance_mm: float, optional
Distance for the propagation (note: this algorithm will be slow for
large distances)
target: {'zero','mask','lower','higher'}, optional
Propagate into zero (default), masked out, lower or higher neighboring voxels
scaling: float, optional
Multiply the propagated values by a factor <=1 (default is 1)
domain: niimg, optional
Domain mask to specify acceptable regions to grow into
save_data: bool
Save output data to file (default is False)
overwrite: bool
Overwrite existing results (default is False)
output_dir: str, optional
Path to desired output directory, will be created if it doesn't exist
file_name: str, optional
Desired base name for output files with file extension
(suffixes will be added)
Returns
----------
dict
Dictionary collecting outputs under the following keys
(suffix of output files in brackets)
* result (niimg): The propagated intensity image
Notes
----------
Original Java module by Pierre-Louis Bazin.
"""
print('\nIntensity Propagation')
# make sure that saving related parameters are correct
if save_data:
output_dir = _output_dir_4saving(output_dir, image)
out_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=image,
suffix='ppag-img'))
if overwrite is False \
and os.path.isfile(out_file) :
print("skip computation (use existing results)")
output = {'result': out_file}
return output
# start virtual machine, if not already running
try:
mem = _check_available_memory()
nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max'])
except ValueError:
pass
# create instance
propag = nighresjava.IntensityPropagate()
# set parameters
# load image and use it to set dimensions and resolution
img = load_volume(image)
data = img.get_fdata()
affine = img.affine
header = img.header
resolution = [x.item() for x in header.get_zooms()]
dimensions = data.shape
if len(dimensions)>2:
propag.setDimensions(dimensions[0], dimensions[1], dimensions[2])
propag.setResolutions(resolution[0], resolution[1], resolution[2])
else:
propag.setDimensions(dimensions[0], dimensions[1])
propag.setResolutions(resolution[0], resolution[1])
propag.setInputImage(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
if mask is not None:
propag.setMaskImage(nighresjava.JArray('int')(
(load_volume(mask).get_fdata().flatten('F')).astype(int).tolist()))
if domain is not None:
propag.setDomainImage(nighresjava.JArray('int')(
(load_volume(domain).get_fdata().flatten('F')).astype(int).tolist()))
# set algorithm parameters
propag.setCombinationMethod(combine)
propag.setPropagationDistance(distance_mm)
propag.setTargetVoxels(target)
propag.setPropogationScalingFactor(scaling)
# execute the algorithm
try:
propag.execute()
except:
# if the Java module fails, reraise the error it throws
print("\n The underlying Java code did not execute cleanly: ")
print(sys.exc_info()[0])
raise
return
# reshape output to what nibabel likes
propag_data = np.reshape(np.array(propag.getResultImage(),
dtype=np.float32), dimensions, 'F')
# adapt header max for each image so that correct max is displayed
# and create nifiti objects
header['cal_min'] = np.nanmin(propag_data)
header['cal_max'] = np.nanmax(propag_data)
out = nb.Nifti1Image(propag_data, affine, header)
if save_data:
save_volume(out_file, out)
return {'result': out_file}
else:
return {'result': out}
def mesh_data_to_volume(surface_mesh,
reference_image,
combine='mean',
distance_mm=1.0,
center=False,
rescale=None,
save_data=False,
overwrite=False,
output_dir=None,
file_name=None):
""" Mesh data to volume
Projects mesh data onto voxel space and propagates the values into the neighboring voxels
Parameters
----------
surface_mesh: mesh
Mesh model of the surface
reference_image: niimg
Image of the dimensions and resolutions corresponding to the mesh
combine: {'min','mean','max'}, optional
Propagate using the mean (default), max or min data from neighboring voxels
distance_mm: float, optional
Distance for the propagation (note: this algorithm will be slow for
large distances)
center: bool
Use image center as origin (default is False)
rescale: float or array
Rescale the data by a given factor (or factors for each axis, default is None)
save_data: bool
Save output data to file (default is False)
overwrite: bool
Overwrite existing results (default is False)
output_dir: str, optional
Path to desired output directory, will be created if it doesn't exist
file_name: str, optional
Desired base name for output files with file extension
(suffixes will be added)
Returns
----------
dict
Dictionary collecting outputs under the following keys
(suffix of output files in brackets)
* result (niimg): The propagated mesh data image
Notes
----------
Original Java module by Pierre-Louis Bazin.
"""
print('\nMesh data to volume')
# make sure that saving related parameters are correct
if save_data:
output_dir = _output_dir_4saving(output_dir, surface_mesh)
out_file = os.path.join(
output_dir,
_fname_4saving(module=__name__,
file_name=file_name,
rootfile=surface_mesh,
suffix='mdtv-img',
ext="nii.gz"))
if overwrite is False \
and os.path.isfile(out_file) :
print("skip computation (use existing results)")
output = {'result': out_file}
return output
# start virtual machine, if not already running
try:
mem = _check_available_memory()
nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max'])
except ValueError:
pass
# create instance
propag = nighresjava.IntensityPropagate()
# set parameters
# load the data
mesh = load_mesh(surface_mesh)
# load image and use it to set dimensions and resolution
img = load_volume(reference_image)
affine = img.affine
header = img.header
resolution = [x.item() for x in header.get_zooms()]
dimensions = img.header.get_data_shape()
# recenter to middle of the image if required
cx = 0.0
cy = 0.0
cz = 0.0
if center is not None:
if isinstance(center, list):
cx = center[0]
cy = center[1]
cz = center[2]
elif center:
cx = dimensions[0] / 2.0
cy = dimensions[1] / 2.0
cz = dimensions[2] / 2.0
#rescale if required
rx = 1.0
ry = 1.0
rz = 1.0
if rescale is not None:
if isinstance(rescale, list):
rx = rescale[0]
ry = rescale[1]
rz = rescale[2]
else:
rx = rescale
ry = rescale
rz = rescale
# paste the mesh values onto the reference image space
data = numpy.zeros(dimensions)
for idx, val in enumerate(mesh['points']):
x = int(round(mesh['points'][idx][0] * rx + cx))
y = int(round(mesh['points'][idx][1] * ry + cy))
z = int(round(mesh['points'][idx][2] * rz + cz))
if (x >= 0 and x < dimensions[0] and y >= 0 and y < dimensions[1]
and z >= 0 and z < dimensions[2]):
data[x, y, z] = mesh['data'][idx][0]
propag.setDimensions(dimensions[0], dimensions[1], dimensions[2])
propag.setResolutions(resolution[0], resolution[1], resolution[2])
propag.setInputImage(
nighresjava.JArray('float')((data.flatten('F')).astype(float)))
# set algorithm parameters
propag.setCombinationMethod(combine)
propag.setPropagationDistance(distance_mm)
propag.setTargetVoxels('zero')
propag.setPropogationScalingFactor(1.0)
# execute the algorithm
try:
propag.execute()
except:
# if the Java module fails, reraise the error it throws
print("\n The underlying Java code did not execute cleanly: ")
print(sys.exc_info()[0])
raise
return
# reshape output to what nibabel likes
propag_data = numpy.reshape(
numpy.array(propag.getResultImage(), dtype=numpy.float32), dimensions,
'F')
# adapt header max for each image so that correct max is displayed
# and create nifiti objects
header['cal_min'] = numpy.nanmin(propag_data)
header['cal_max'] = numpy.nanmax(propag_data)
out = nibabel.Nifti1Image(propag_data, affine, header)
if save_data:
save_volume(out_file, out)
return {'result': out_file}
else:
return {'result': out}