Source code for nighres.intensity.intensity_propagation

import numpy as np
import nibabel as nb
import os
import sys
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 intensity_propagation(image, mask=None, combine='mean', distance_mm=5.0, target='zero', scaling=1.0, save_data=False, overwrite=False, output_dir=None, file_name=None): """ Intensity Propogation Propagates the values inside the mask (or non-zero) into the neighboring voxels Parameters ---------- image: niimg Input image mask: niimg, optional Data mask to specify acceptable seeding regions combine: {'min','mean','max'}, optional Propagate using the mean (default), max or min data from neighboring voxels distance_mm: float, optional Distance for the propagation (note: this algorithm will be slow for large distances) target: {'zero','mask','lower','higher'}, optional Propagate into zero (default), masked out, lower or higher neighboring voxels scaling: float, optional Multiply the propagated values by a factor <=1 (default is 1) save_data: bool Save output data to file (default is False) overwrite: bool 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): The propagated intensity image Notes ---------- Original Java module by Pierre-Louis Bazin. """ print('\nIntensity Propagation') # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, image) out_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=image, suffix='ppag-img')) if overwrite is False \ and os.path.isfile(out_file) : print("skip computation (use existing results)") output = {'result': out_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 instance propag = nighresjava.IntensityPropagate() # set parameters # load image and use it to set dimensions and resolution img = load_volume(image) data = img.get_data() affine = img.affine header = img.header resolution = [x.item() for x in header.get_zooms()] dimensions = data.shape propag.setDimensions(dimensions[0], dimensions[1], dimensions[2]) propag.setResolutions(resolution[0], resolution[1], resolution[2]) propag.setInputImage(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) if mask is not None: propag.setMaskImage(idx, nighresjava.JArray('int')( (load_volume(mask).get_data().flatten('F')).astype(int).tolist())) # set algorithm parameters propag.setCombinationMethod(combine) propag.setPropagationDistance(distance_mm) propag.setTargetVoxels(target) propag.setPropogationScalingFactor(scaling) # execute the algorithm try: propag.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 # reshape output to what nibabel likes propag_data = np.reshape(np.array(propag.getResultImage(), dtype=np.float32), dimensions, 'F') # adapt header max for each image so that correct max is displayed # and create nifiti objects header['cal_min'] = np.nanmin(propag_data) header['cal_max'] = np.nanmax(propag_data) out = nb.Nifti1Image(propag_data, affine, header) if save_data: save_volume(out_file, out) return {'result': out_file} else: return {'result': out}