Source code for nighres.shape.levelset_fusion

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_topology_lut_dir, _check_available_memory

[docs]def levelset_fusion(levelset_images, correct_topology=True, topology_lut_dir=None, save_data=False, overwrite=False, output_dir=None, file_name=None): """Levelset fusion Creates an average levelset surface representations from a collection of levelset surfaces, with same avearage volume and (optionally) spherical topology Parameters ---------- levelset_images: niimg List of levelset images to combine. correct_topology: bool, optional Corrects the average shape to ensure correct topology (default is True) topology_lut_dir: str, optional Path to directory in which topology files are stored (default is stored in TOPOLOGY_LUT_DIR) 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 combined surface (_lsf-avg) Notes ---------- Original Java module by Pierre-Louis Bazin """ print("\nLevelset Shape Fusion") # check topology_lut_dir and set default if not given topology_lut_dir = _check_topology_lut_dir(topology_lut_dir) # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, levelset_images[0]) levelset_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=levelset_images[0], suffix='lsf-avg')) print('output file: '+levelset_file) 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 algorithm = nighresjava.ShapeLevelsetFusion() # load the data nsubjects = len(levelset_images) img = load_volume(levelset_images[0]) hdr = img.header aff = img.affine resolution = [x.item() for x in hdr.get_zooms()] dimensions = img.get_data().shape algorithm.setNumberOfImages(nsubjects) algorithm.setResolutions(resolution[0], resolution[1], resolution[2]) algorithm.setDimensions(dimensions[0], dimensions[1], dimensions[2]) levelset_data = []; for idx in range(len(levelset_images)): img = load_volume(levelset_images[idx]) data = img.get_data() algorithm.setLevelsetImageAt(idx, nighresjava.JArray('float')( (data.flatten('F')).astype(float))) algorithm.setCorrectSkeletonTopology(correct_topology) algorithm.setTopologyLUTdirectory(topology_lut_dir) # execute class try: algorithm.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(algorithm.getLevelsetAverage(), dtype=np.float32), dimensions, 'F') hdr['cal_min'] = np.nanmin(levelset_data) 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}