import numpy as np
import nibabel as nb
import os
import sys
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 recursive_ridge_diffusion(input_image, ridge_intensities, ridge_filter,
surface_levelset=None, orientation='undefined',
loc_prior=None,
min_scale=0, max_scale=3,
diffusion_factor=1.0,
similarity_scale=0.1,
max_iter=100, max_diff=1e-3,
threshold=0.5,
save_data=False, overwrite=False, output_dir=None,
file_name=None):
""" Recursive Ridge Diffusion
Extracts planar of tubular structures across multiple scales, with an
optional directional bias.
Parameters
----------
input_image: niimg
Input image
ridge_intensities: {'bright','dark','both'}
Which intensities to consider for the filtering
ridge_filter: {'2D','1D','0D'}
Whether to filter for 2D ridges, 1D vessels, or 0D holes
surface_levelset: niimg, optional
Level set surface to restrict the orientation of the detected features
orientation: {'undefined','parallel','orthogonal'}
The orientation of features to keep with regard to the surface or its normal
loc_prior: niimg, optional
Location prior image to restrict the search for features
min_scale: int
Minimum scale (in voxels) to look for features (default is 0)
max_scale: int
Maximum scale (in voxels) to look for features (default is 3)
diffusion_factor: float
Scaling factor for the diffusion weighting in [0,1] (default is 1.0)
similarity_scale: float
Scaling of the similarity function as a factor of intensity range
max_iter: int
Maximum number of diffusion iterations
max_diff: int
Maximum difference to stop the diffusion
threshold: float
Detection threshold for the structures to keep (default is 0.5)
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)
* filter (niimg): raw filter response (_rrd-filter)
* propagation (niimg): propagated probabilistic response after diffusion (_rrd-propag)
* scale (niimg): scale of the detection filter (_rrd-scale)
* ridge_dir (niimg): estimated local ridge direction (_rrd-dir)
* ridge_pv (niimg): ridge partial volume map, taking size into account (_rrd-pv)
* ridge_size (niimg): estimated size of each detected component (rrd-size)
Notes
----------
Original Java module by Pierre-Louis Bazin. Extension of the recursive ridge
filter in [1]_.
References
----------
.. [1] Bazin et al (2016), Vessel segmentation from quantitative
susceptibility maps for local oxygenation venography, Proc ISBI.
"""
print('\n Recursive Ridge Diffusion')
# check atlas_file and set default if not given
#atlas_file = _check_atlas_file(atlas_file)
# make sure that saving related parameters are correct
if save_data:
output_dir = _output_dir_4saving(output_dir, input_image)
filter_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=input_image,
suffix='rrd-filter'))
propagation_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=input_image,
suffix='rrd-propag'))
scale_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=input_image,
suffix='rrd-scale'))
ridge_direction_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=input_image,
suffix='rrd-dir'))
ridge_pv_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=input_image,
suffix='rrd-pv'))
ridge_size_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=input_image,
suffix='rrd-size'))
if overwrite is False \
and os.path.isfile(filter_file) \
and os.path.isfile(propagation_file) \
and os.path.isfile(scale_file) \
and os.path.isfile(ridge_direction_file) \
and os.path.isfile(ridge_pv_file) \
and os.path.isfile(ridge_size_file) :
print("skip computation (use existing results)")
output = {'filter': filter_file,
'propagation': propagation_file,
'scale': scale_file,
'ridge_dir': ridge_direction_file,
'ridge_pv': ridge_pv_file,
'ridge_size': ridge_size_file}
return output
# load input image and use it to set dimensions and resolution
img = load_volume(input_image)
data = img.get_data()
affine = img.affine
header = img.header
resolution = [x.item() for x in header.get_zooms()]
dimensions = data.shape
if (len(dimensions)<3): dimensions = (dimensions[0], dimensions[1], 1)
if (len(resolution)<3): resolution = [resolution[0], resolution[1], 1.0]
# start virtual machine, if not already running
try:
mem = _check_available_memory()
nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max'])
except ValueError:
pass
# create extraction instance
if dimensions[2] is 1: rrd = nighresjava.FilterRecursiveRidgeDiffusion2D()
else: rrd = nighresjava.FilterRecursiveRidgeDiffusion()
# set parameters
rrd.setRidgeIntensities(ridge_intensities)
rrd.setRidgeFilter(ridge_filter)
rrd.setOrientationToSurface(orientation)
rrd.setMinimumScale(min_scale)
rrd.setMaximumScale(max_scale)
rrd.setDiffusionFactor(diffusion_factor)
rrd.setSimilarityScale(similarity_scale)
rrd.setPropagationModel("none")
if max_iter>0: rrd.setPropagationModel("diffusion")
rrd.setMaxIterations(max_iter)
rrd.setMaxDifference(max_diff)
rrd.setDetectionThreshold(threshold)
rrd.setDimensions(dimensions[0], dimensions[1], dimensions[2])
rrd.setResolutions(resolution[0], resolution[1], resolution[2])
# input input_image
rrd.setInputImage(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
# input surface_levelset : dirty fix for the case where surface image not input
try:
data = load_volume(surface_levelset).get_data()
rrd.setSurfaceLevelSet(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
except:
print("no surface image")
# input location prior image : loc_prior is optional
try:
data = load_volume(loc_prior).get_data()
rrd.setLocationPrior(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
except:
print("no location prior image")
# execute Extraction
try:
rrd.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
filter_data = np.reshape(np.array(rrd.getFilterResponseImage(),
dtype=np.float32), dimensions, 'F')
propagation_data = np.reshape(np.array(rrd.getPropagatedResponseImage(),
dtype=np.float32), dimensions, 'F')
scale_data = np.reshape(np.array(rrd.getDetectionScaleImage(),
dtype=np.int32), dimensions, 'F')
if dimensions[2] is 1:
ridge_direction_data = np.reshape(np.array(rrd.getRidgeDirectionImage(),
dtype=np.float32),
(dimensions[0],dimensions[1],2),
'F')
else:
ridge_direction_data = np.reshape(np.array(rrd.getRidgeDirectionImage(),
dtype=np.float32),
(dimensions[0],dimensions[1],dimensions[2],3),
'F')
ridge_pv_data = np.reshape(np.array(rrd.getRidgePartialVolumeImage(),
dtype=np.float32), dimensions, 'F')
ridge_size_data = np.reshape(np.array(rrd.getRidgeSizeImage(),
dtype=np.float32), dimensions, 'F')
# adapt header max for each image so that correct max is displayed
# and create nifiti objects
header['cal_max'] = np.nanmax(filter_data)
filter_img = nb.Nifti1Image(filter_data, affine, header)
header['cal_max'] = np.nanmax(propagation_data)
propag_img = nb.Nifti1Image(propagation_data, affine, header)
header['cal_max'] = np.nanmax(scale_data)
scale_img = nb.Nifti1Image(scale_data, affine, header)
header['cal_max'] = np.nanmax(ridge_direction_data)
ridge_dir_img = nb.Nifti1Image(ridge_direction_data, affine, header)
header['cal_max'] = np.nanmax(ridge_pv_data)
ridge_pv_img = nb.Nifti1Image(ridge_pv_data, affine, header)
header['cal_max'] = np.nanmax(ridge_size_data)
ridge_size_img = nb.Nifti1Image(ridge_size_data, affine, header)
if save_data:
save_volume(filter_file, filter_img)
save_volume(propagation_file, propag_img)
save_volume(scale_file, scale_img)
save_volume(ridge_direction_file, ridge_dir_img)
save_volume(ridge_pv_file, ridge_pv_img)
save_volume(ridge_size_file, ridge_size_img)
return {'filter': filter_file, 'propagation': propagation_file, 'scale': scale_file,
'ridge_dir': ridge_direction_file, 'ridge_pv': ridge_pv_file,
'ridge_size': ridge_size_file}
else:
return {'filter': filter_img, 'propagation': propag_img, 'scale': scale_img,
'ridge_dir': ridge_dir_img, 'ridge_pv': ridge_pv_img,
'ridge_size': ridge_size_img}