Source code for nighres.filtering.filter_ridge_structures

import numpy as np
import nibabel as nb
import os
import sys
import nighresjava
from 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 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