# basic dependencies
import os
import sys
# main dependencies: numpy, nibabel
import numpy as np
import nibabel as nb
# nighresjava and nighres functions
import nighresjava
from ..io import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, \
_check_topology_lut_dir, _check_atlas_file, _check_available_memory
[docs]def simple_skeleton(input_image,
shape_image_type = 'signed_distance',
boundary_threshold = 0.0,
skeleton_threshold = 2.0,
topology_lut_dir = None,
save_data=False,
overwrite=False,
output_dir=None,
file_name=None):
""" Simple Skeleton
Create a skeleton for a levelset surface or a probability map (loosely adapted from Bouix et al., 2006)
Parameters
----------
input_image: niimg
Image containing structure-of-interest
shape_image_type: str
Shape of the input image: either 'signed_distance' or 'probability_map'.
boundary_threshold: float
Boundary threshold (>0: inside, <0: outside)
skeleton_threshold: float
Skeleton threshold (>0: inside, <0: outside)
topology_lut_dir:str
Directory of LUT topology
save_data: bool, optional
Save output data to file (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)
* medial (niimg): A 2D medial surface extracted from the shape (_ssk-med)
* skeleton (niimg): The 1D skeleton extracted from the shape (_ssk-skel)
Notes
----------
Original Java module by Pierre-Louis Bazin.
"""
print("\nSimple Skeleton")
# check topology_lut_dir and set default if not given
topology_lut_dir = _check_topology_lut_dir(topology_lut_dir)
# make sure that saving related parameters are correct
if save_data:
output_dir = _output_dir_4saving(output_dir, input_image)
MedialSurface_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=input_image,
suffix='ssk-med'))
Medial_Curve_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=input_image,
suffix='ssk-skel'))
if overwrite is False \
and os.path.isfile(MedialSurface_file) \
and os.path.isfile(Medial_Curve_file) :
print("skip computation (use existing results)")
output = {'medial': MedialSurface_file,
'skeleton': Medial_Curve_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 algorithm instance
skeleton = nighresjava.ShapeSimpleSkeleton()
# set parameters
skeleton.setBoundaryThreshold(boundary_threshold)
skeleton.setSkeletonThreshold(skeleton_threshold)
skeleton.setTopologyLUTdirectory(topology_lut_dir)
skeleton.setShapeImageType(shape_image_type)
# load images and set dimensions and resolution
input_image = load_volume(input_image)
data = input_image.get_data()
affine = input_image.get_affine()
header = input_image.get_header()
resolution = [x.item() for x in header.get_zooms()]
dimensions = input_image.shape
skeleton.setDimensions(dimensions[0], dimensions[1], dimensions[2])
skeleton.setResolutions(resolution[0], resolution[1], resolution[2])
data = load_volume(input_image).get_data()
skeleton.setShapeImage(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
# execute
try:
skeleton.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
# Collect output
medialImage_data = np.reshape(np.array(
skeleton.getMedialSurfaceImage(),
dtype=np.int8), dimensions, 'F')
skelImage_data = np.reshape(np.array(
skeleton.getMedialCurveImage(),
dtype=np.int8), dimensions, 'F')
# adapt header max for each image so that correct max is displayed
# and create nifiti objects
header['cal_min'] = np.nanmin(medialImage_data)
header['cal_max'] = np.nanmax(medialImage_data)
medialImage = nb.Nifti1Image(medialImage_data, affine, header)
header['cal_min'] = np.nanmin(skelImage_data)
header['cal_max'] = np.nanmax(skelImage_data)
skelImage = nb.Nifti1Image(skelImage_data, affine, header)
if save_data:
save_volume(MedialSurface_file, medialImage)
save_volume(Medial_Curve_file, skelImage)
return {'medial': MedialSurface_file,
'skeleton': Medial_Curve_file}
else:
return {'medial': medialImage, 'skeleton': skelImage}