Source code for nighres.shape.topology_correction

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 topology_correction(image, shape_type, connectivity='wcs', propagation='object->background', minimum_distance=0.00001, topology_lut_dir=None, save_data=False, overwrite=False, output_dir=None, file_name=None): """Topology correction Corrects the topology of a binary image, a probability map or a levelset surface to spherical with a fast marching technique [1]_. Parameters ---------- image: niimg Image representing the shape of interest shape_type: {'binary_object','probability_map','signed_distance_function'} Which type of image is used as input connectivity: {'wcs','6/18','6/26','18/6','26/6'} What connectivity type to use (default is wcs: well-composed surfaces) propagation: {'object->background','background->object'} Which direction to use to enforce topology changes (default is 'object->background' ) minimum_distance: float, optional Minimum distance to impose between successive voxels (default is 1e-5) 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) * corrected (niimg): Corrected image (output file suffix _tpc-img) * object (niimg): Corrected binary object (output file suffix _tpc-obj) Notes ---------- Original Java module by Pierre-Louis Bazin References ---------- .. [1] Bazin and Pham (2007). Topology correction of segmented medical images using a fast marching algorithm doi:10.1016/j.cmpb.2007.08.006 """ print("\nTopology Correction") # 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, image) corrected_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=image, suffix='tpc-img')) corrected_obj_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=image, suffix='tpc-obj')) if overwrite is False \ and os.path.isfile(corrected_file) \ and os.path.isfile(corrected_obj_file) : print("skip computation (use existing results)") output = {'corrected': corrected_file, 'object': corrected_obj_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.ShapeTopologyCorrection2() # load the data img = load_volume(image) hdr = img.header aff = img.affine data = img.get_data() resolution = [x.item() for x in hdr.get_zooms()] dimensions = data.shape algorithm.setResolutions(resolution[0], resolution[1], resolution[2]) algorithm.setDimensions(dimensions[0], dimensions[1], dimensions[2]) algorithm.setShapeImage(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) algorithm.setShapeImageType(shape_type) algorithm.setTopology(connectivity) algorithm.setTopologyLUTdirectory(topology_lut_dir) algorithm.setPropagationDirection(propagation) algorithm.setMinimumDistance(minimum_distance) # 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 corrected_data = np.reshape(np.array(algorithm.getCorrectedImage(), dtype=np.float32), dimensions, 'F') hdr['cal_min'] = np.nanmin(corrected_data) hdr['cal_max'] = np.nanmax(corrected_data) corrected = nb.Nifti1Image(corrected_data, aff, hdr) corrected_obj_data = np.reshape(np.array(algorithm.getCorrectedObjectImage(), dtype=np.int32), dimensions, 'F') hdr['cal_min'] = np.nanmin(corrected_obj_data) hdr['cal_max'] = np.nanmax(corrected_obj_data) corrected_obj = nb.Nifti1Image(corrected_obj_data, aff, hdr) if save_data: save_volume(corrected_file, corrected) save_volume(corrected_obj_file, corrected_obj) return {'corrected': corrected_file, 'object': corrected_obj_file} else: return {'corrected': corrected, 'object': corrected_obj}