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_available_memory
[docs]def filter_ridge_structures(input_image,
structure_intensity='bright',
output_type='probability',
use_strict_min_max_filter=True,
save_data=False, overwrite=False, output_dir=None,
file_name=None):
""" Filter Ridge Structures
Uses an image filter to make a probabilistic image of ridge
structures.
Parameters
----------
input_image: niimg
Image containing structure-of-interest
structure_intensity: {'bright', 'dark', 'both}
Image intensity of structure-of-interest'
output_type: {'probability','intensity'}
Whether the image should be normalized to reflect probabilities
use_strict_min_max_filter: bool, optional
Choose between the more specific recursive ridge filter or a more
sensitive bidirectional filter (default is True)
save_data: bool, optional
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)
* ridge_structure_image: Image that reflects the presensence of ridges
in the image (_rdg-img)
Notes
----------
Original Java module by Pierre-Louis Bazin.
"""
if save_data:
output_dir = _output_dir_4saving(output_dir, input_image)
ridge_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=input_image,
suffix='rdg-img', ))
if overwrite is False \
and os.path.isfile(ridge_file) :
print("skip computation (use existing results)")
output = {'result': ridge_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
filter_ridge = nighresjava.FilterRidgeStructures()
# set parameters
filter_ridge.setStructureIntensity(structure_intensity)
filter_ridge.setOutputType(output_type)
filter_ridge.setUseStrictMinMaxFilter(use_strict_min_max_filter)
# load images and set dimensions and resolution
input_image = load_volume(input_image)
data = input_image.get_data()
affine = input_image.affine
header = input_image.header
resolution = [x.item() for x in header.get_zooms()]
dimensions = input_image.shape
filter_ridge.setDimensions(dimensions[0], dimensions[1], dimensions[2])
filter_ridge.setResolutions(resolution[0], resolution[1], resolution[2])
data = load_volume(input_image).get_data()
filter_ridge.setInputImage(nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
# execute
try:
filter_ridge.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
ridge_structure_image_data = np.reshape(np.array(
filter_ridge.getRidgeStructureImage(),
dtype=np.float32), dimensions, 'F')
if output_type == 'probability':
header['cal_min'] = 0.0
header['cal_max'] = 1.0
else:
header['cal_min'] = np.nanmin(ridge_structure_image_data)
header['cal_max'] = np.nanmax(ridge_structure_image_data)
ridge_structure_image = nb.Nifti1Image(ridge_structure_image_data, affine,
header)
if save_data:
save_volume(ridge_file, ridge_structure_image)
outputs = {'result': ridge_file}
else:
outputs = {'result': ridge_structure_image}
return outputs