Source code for nighres.surface.probability_to_levelset

import os
import sys
import numpy as np
import nibabel as nb
import nighresjava
from import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, _check_available_memory

[docs]def probability_to_levelset(probability_image, mask_image=None, save_data=False, overwrite=False, output_dir=None, file_name=None): """Levelset from probability map Creates a levelset surface representations from a probabilistic map or a mask. The levelset indicates each voxel's distance to the closest boundary. It takes negative values inside and positive values outside of the object. Parameters ---------- probability_image: niimg Probability image to be turned into levelset. Values should be in [0, 1], either a binary mask or defining the boundary at 0.5. mask_image: niimg, optional Mask image defining the region in which to compute the levelset. Values equal to zero are set to maximum distance. 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) * result (niimg): Levelset representation of surface (_p2l-surf) Notes ---------- Original Java module by Pierre-Louis Bazin """ print("\nProbability to Levelset") # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, probability_image) levelset_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=probability_image, suffix='p2l-surf')) if overwrite is False \ and os.path.isfile(levelset_file) : print("skip computation (use existing results)") output = {'result': levelset_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 prob2level = nighresjava.SurfaceProbabilityToLevelset() # load the data prob_img = load_volume(probability_image) prob_data = prob_img.get_data() hdr = prob_img.header aff = prob_img.affine resolution = [x.item() for x in hdr.get_zooms()] dimensions = prob_data.shape # set parameters from input data prob2level.setProbabilityImage(nighresjava.JArray('float')( (prob_data.flatten('F')).astype(float))) if (mask_image is not None): mask_data = load_volume(mask_image).get_data() prob2level.setMaskImage(nighresjava.JArray('int')( (mask_data.flatten('F')).astype(int).tolist())) if len(dimensions)>2: prob2level.setResolutions(resolution[0], resolution[1], resolution[2]) prob2level.setDimensions(dimensions[0], dimensions[1], dimensions[2]) else: prob2level.setResolutions(resolution[0], resolution[1], 1.0) prob2level.setDimensions(dimensions[0], dimensions[1], 1) # execute class try: prob2level.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 levelset_data = np.reshape(np.array(prob2level.getLevelSetImage(), dtype=np.float32), dimensions, 'F') hdr['cal_max'] = np.nanmax(levelset_data) levelset = nb.Nifti1Image(levelset_data, aff, hdr) if save_data: save_volume(levelset_file, levelset) return {'result': levelset_file} else: return {'result': levelset}