Source code for nighres.intensity.background_estimation

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 background_estimation(image, distribution='exponential', ratio=1e-3, skip_zero=True, iterate=True, dilate=0, threshold=0.5, save_data=False, overwrite=False, output_dir=None, file_name=None): """ Background Estimation Estimates image background by robustlyfitting various distribution models Parameters ---------- image: niimg Input image distribution: {'exponential','half-normal'} Distribution model to use for the background noise ratio: float, optional Robustness ratio for estimating image intensities skip_zero: bool, optional Whether to consider or skip zero values iterate: bool, optional Whether to run an iterative estimation (preferred, but sometimes unstable) dilate: int, optional Number of voxels to dilate or erode in the final mask 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) * masked (niimg): The background-masked input image * proba (niimg): The probability map of the foreground * mask (niimg): The mask of the foreground Notes ---------- Original Java module by Pierre-Louis Bazin. """ print('\nBackground Estimation') # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, image) masked_file = os.path.join(output_dir, _fname_4saving(file_name=file_name, rootfile=image, suffix='bge-masked')) proba_file = os.path.join(output_dir, _fname_4saving(file_name=file_name, rootfile=image, suffix='bge-proba')) mask_file = os.path.join(output_dir, _fname_4saving(file_name=file_name, rootfile=image, suffix='bge-mask')) if overwrite is False \ and os.path.isfile(masked_file) and os.path.isfile(proba_file) \ and os.path.isfile(mask_file) : print("skip computation (use existing results)") output = {'masked': load_volume(masked_file), 'proba': load_volume(proba_file), 'mask': load_volume(mask_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 bge = nighresjava.IntensityBackgroundEstimator2() # 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 if len(dimensions)>2: bge.setDimensions(dimensions[0], dimensions[1], dimensions[2]) #bge.setResolutions(resolution[0], resolution[1], resolution[2]) else: bge.setDimensions(dimensions[0], dimensions[1], 1) #bge.setResolutions(resolution[0], resolution[1], 1) bge.setInputImage(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) # set algorithm parameters bge.setBackgroundDistribution(distribution) bge.setRobustMinMaxThresholding(ratio) bge.setSkipZeroValues(skip_zero) bge.setIterative(iterate) bge.setDilateMask(dilate) bge.setMaskThreshold(threshold) # execute the algorithm try: bge.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 masked_data = np.reshape(np.array(bge.getMaskedImage(), 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(masked_data) header['cal_max'] = np.nanmax(masked_data) masked = nb.Nifti1Image(masked_data, affine, header) proba_data = np.reshape(np.array(bge.getProbaImage(), 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(proba_data) header['cal_max'] = np.nanmax(proba_data) proba = nb.Nifti1Image(proba_data, affine, header) mask_data = np.reshape(np.array(bge.getMask(), 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(mask_data) header['cal_max'] = np.nanmax(mask_data) mask = nb.Nifti1Image(mask_data, affine, header) if save_data: save_volume(masked_file, masked) save_volume(proba_file, proba) save_volume(mask_file, mask) return {'masked': masked, 'proba': proba, 'mask': mask}