Source code for nighres.shape.simple_skeleton

# basic dependencies
import os
import sys

# main dependencies: numpy, nibabel
import numpy as np
import nibabel as nb

# nighresjava and nighres functions
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 simple_skeleton(input_image, shape_image_type = 'signed_distance', boundary_threshold = 0.0, skeleton_threshold = 2.0, topology_lut_dir = None, save_data=False, overwrite=False, output_dir=None, file_name=None): """ Simple Skeleton Create a skeleton for a levelset surface or a probability map (loosely adapted from Bouix et al., 2006) Parameters ---------- input_image: niimg Image containing structure-of-interest shape_image_type: str Shape of the input image: either 'signed_distance' or 'probability_map'. boundary_threshold: float Boundary threshold (>0: inside, <0: outside) skeleton_threshold: float Skeleton threshold (>0: inside, <0: outside) topology_lut_dir:str Directory of LUT topology save_data: bool, optional Save output data to file (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) * medial (niimg): A 2D medial surface extracted from the shape (_ssk-med) * skeleton (niimg): The 1D skeleton extracted from the shape (_ssk-skel) Notes ---------- Original Java module by Pierre-Louis Bazin. """ print("\nSimple Skeleton") # 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, input_image) MedialSurface_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=input_image, suffix='ssk-med')) Medial_Curve_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=input_image, suffix='ssk-skel')) if overwrite is False \ and os.path.isfile(MedialSurface_file) \ and os.path.isfile(Medial_Curve_file) : print("skip computation (use existing results)") output = {'medial': MedialSurface_file, 'skeleton': Medial_Curve_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 skeleton = nighresjava.ShapeSimpleSkeleton() # set parameters skeleton.setBoundaryThreshold(boundary_threshold) skeleton.setSkeletonThreshold(skeleton_threshold) skeleton.setTopologyLUTdirectory(topology_lut_dir) skeleton.setShapeImageType(shape_image_type) # load images and set dimensions and resolution input_image = load_volume(input_image) data = input_image.get_data() affine = input_image.get_affine() header = input_image.get_header() resolution = [x.item() for x in header.get_zooms()] dimensions = input_image.shape skeleton.setDimensions(dimensions[0], dimensions[1], dimensions[2]) skeleton.setResolutions(resolution[0], resolution[1], resolution[2]) data = load_volume(input_image).get_data() skeleton.setShapeImage(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) # execute try: skeleton.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 medialImage_data = np.reshape(np.array( skeleton.getMedialSurfaceImage(), dtype=np.int8), dimensions, 'F') skelImage_data = np.reshape(np.array( skeleton.getMedialCurveImage(), dtype=np.int8), dimensions, 'F') # adapt header max for each image so that correct max is displayed # and create nifiti objects header['cal_min'] = np.nanmin(medialImage_data) header['cal_max'] = np.nanmax(medialImage_data) medialImage = nb.Nifti1Image(medialImage_data, affine, header) header['cal_min'] = np.nanmin(skelImage_data) header['cal_max'] = np.nanmax(skelImage_data) skelImage = nb.Nifti1Image(skelImage_data, affine, header) if save_data: save_volume(MedialSurface_file, medialImage) save_volume(Medial_Curve_file, skelImage) return {'medial': MedialSurface_file, 'skeleton': Medial_Curve_file} else: return {'medial': medialImage, 'skeleton': skelImage}