Source code for nighres.surface.levelset_to_probability

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

[docs]def levelset_to_probability(levelset_image, distance_mm=5, save_data=False, overwrite=False, output_dir=None, file_name=None): """Levelset to probability Creates a probability map from the distance to a levelset surface representation. Parameters ---------- levelset_image: niimg Levelset image to be turned into probabilities distance_mm: float, optional Distance used for the range of probabilities in ]0,1[ 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): Probability map (output file suffix _l2p-proba) Notes ---------- Ported from original Java module by Pierre-Louis Bazin """ print("\nLevelset to Probabilities") # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, levelset_image) proba_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=levelset_image, suffix='l2p-proba')) if overwrite is False \ and os.path.isfile(proba_file) : print("skip computation (use existing results)") output = {'result': proba_file} return output # load the data lvl_img = load_volume(levelset_image) lvl_data = lvl_img.get_data() hdr = lvl_img.header aff = lvl_img.affine resolution = [x.item() for x in hdr.get_zooms()] dimensions = lvl_data.shape # algorithm distance = distance_mm/np.min(resolution) print("voxel spread: "+str(distance)) p_data = 0.5*(np.maximum(-1,np.minimum(1,-lvl_data/distance))+1) hdr['cal_min'] = np.nanmin(p_data) hdr['cal_max'] = np.nanmax(p_data) proba = nb.Nifti1Image(p_data, aff, hdr) if save_data: save_volume(proba_file, proba) return {'result': proba_file} else: return {'result': proba}