Source code for nighres.microscopy.stack_intensity_mapping

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 stack_intensity_mapping(image, references, mapped, weights = None, patch=2, search=3, save_data=False, overwrite=False, output_dir=None, file_name=None): """ Stack intensity mapping Uses a simple non-local means approach adapted from [1]_ Parameters ---------- image: niimg Input 2D image references: [niimg] Reference 2D images to use for intensity mapping mapped: [niimg] Corresponding mapped 2D images to use for intensity mapping weights: [float], optional Weight factors for the 2D images (default is 1 for all) patch: int, optional Maximum distance to define patch size (default is 2) search: int, optional Maximum distance to define search window size (default is 3) 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 intensity mapped input Notes ---------- Original Java module by Pierre-Louis Bazin. References ---------- .. [1] P. Coupé, J.V. Manjón, V. Fonov, J. Pruessner, M. Robles, D.L. Collins, Patch-based segmentation using expert priors: Application to hippocampus and ventricle msegmentation, NeuroImage, vol. 54, pp. 940--954, 2011. """ print('\nStack Intensity Mapping') # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, image) result_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=image, suffix='sim-img')) if overwrite is False \ and os.path.isfile(result_file) : print("skip computation (use existing results)") output = {'result': result_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 sim = nighresjava.NonlocalIntensityMapping() # set parameters # load image and use it to set dimensions and resolution img = load_volume(image) data = img.get_fdata() affine = img.affine header = img.header resolution = [x.item() for x in header.get_zooms()] dimensions = data.shape sim.setDimensions(dimensions[0], dimensions[1], 1) sim.setInputImage(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) sim.setReferenceNumber(len(references)) for idx,ref in enumerate(references): data = load_volume(ref).get_fdata() sim.setReferenceImageAt(idx,nighresjava.JArray('float')( (data.flatten('F')).astype(float))) data = load_volume(mapped[idx]).get_fdata() sim.setMappedImageAt(idx,nighresjava.JArray('float')( (data.flatten('F')).astype(float))) if weights is not None: sim.setWeightAt(idx, weights[idx]) else: sim.setWeightAt(idx, 1.0) # set algorithm parameters sim.setPatchDistance(patch) sim.setSearchDistance(search) # execute the algorithm try: sim.execute2D() 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 data = np.reshape(np.array(sim.getMappedImage(), 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(data) header['cal_max'] = np.nanmax(data) result = nb.Nifti1Image(data, affine, header) if save_data: save_volume(result_file, result) return {'result': result_file} else: return {'result': result}