import os
import sys
import numpy as np
import nibabel as nb
import nighresjava
from ..io import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, \
_check_topology_lut_dir, _check_available_memory
[docs]def topology_correction(image, shape_type,
connectivity='wcs', propagation='object->background',
minimum_distance=0.00001, topology_lut_dir=None,
save_data=False, overwrite=False, output_dir=None,
file_name=None):
"""Topology correction
Corrects the topology of a binary image, a probability map or a levelset
surface to spherical with a fast marching technique [1]_.
Parameters
----------
image: niimg
Image representing the shape of interest
shape_type: {'binary_object','probability_map','signed_distance_function'}
Which type of image is used as input
connectivity: {'wcs','6/18','6/26','18/6','26/6'}
What connectivity type to use (default is wcs: well-composed surfaces)
propagation: {'object->background','background->object'}
Which direction to use to enforce topology changes
(default is 'object->background' )
minimum_distance: float, optional
Minimum distance to impose between successive voxels (default is 1e-5)
topology_lut_dir: str, optional
Path to directory in which topology files are stored (default is stored
in TOPOLOGY_LUT_DIR)
save_data: bool, optional
Save output data to file (default is False)
overwrite: bool, optional
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)
* corrected (niimg): Corrected image (output file suffix _tpc-img)
* object (niimg): Corrected binary object (output file suffix _tpc-obj)
Notes
----------
Original Java module by Pierre-Louis Bazin
References
----------
.. [1] Bazin and Pham (2007). Topology correction of segmented medical
images using a fast marching algorithm
doi:10.1016/j.cmpb.2007.08.006
"""
print("\nTopology Correction")
# 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, image)
corrected_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=image,
suffix='tpc-img'))
corrected_obj_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=image,
suffix='tpc-obj'))
if overwrite is False \
and os.path.isfile(corrected_file) \
and os.path.isfile(corrected_obj_file) :
print("skip computation (use existing results)")
output = {'corrected': corrected_file,
'object': corrected_obj_file}
return output
# start virtual machine if not running
try:
mem = _check_available_memory()
nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max'])
except ValueError:
pass
# initiate class
algorithm = nighresjava.ShapeTopologyCorrection2()
# load the data
img = load_volume(image)
hdr = img.header
aff = img.affine
data = img.get_data()
resolution = [x.item() for x in hdr.get_zooms()]
dimensions = data.shape
algorithm.setResolutions(resolution[0], resolution[1], resolution[2])
algorithm.setDimensions(dimensions[0], dimensions[1], dimensions[2])
algorithm.setShapeImage(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
algorithm.setShapeImageType(shape_type)
algorithm.setTopology(connectivity)
algorithm.setTopologyLUTdirectory(topology_lut_dir)
algorithm.setPropagationDirection(propagation)
algorithm.setMinimumDistance(minimum_distance)
# execute class
try:
algorithm.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 outputs
corrected_data = np.reshape(np.array(algorithm.getCorrectedImage(),
dtype=np.float32), dimensions, 'F')
hdr['cal_min'] = np.nanmin(corrected_data)
hdr['cal_max'] = np.nanmax(corrected_data)
corrected = nb.Nifti1Image(corrected_data, aff, hdr)
corrected_obj_data = np.reshape(np.array(algorithm.getCorrectedObjectImage(),
dtype=np.int32), dimensions, 'F')
hdr['cal_min'] = np.nanmin(corrected_obj_data)
hdr['cal_max'] = np.nanmax(corrected_obj_data)
corrected_obj = nb.Nifti1Image(corrected_obj_data, aff, hdr)
if save_data:
save_volume(corrected_file, corrected)
save_volume(corrected_obj_file, corrected_obj)
return {'corrected': corrected_file, 'object': corrected_obj_file}
else:
return {'corrected': corrected, 'object': corrected_obj}