Source code for nighres.filtering.total_variation_filtering

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 total_variation_filtering(image, mask=None, lambda_scale=0.05, tau_step=0.125,max_dist=1e-4,max_iter=500, save_data=False, overwrite=False, output_dir=None, file_name=None): """ Total Variation Filtering Total variation filtering. Parameters ---------- image: niimg Input image to filter mask: niimg, optional Data mask for processing lambda_scale: float, optional Relative intensity scale for total variation smoothing (default is 0.5) tau_step: float, optional Internal step parameter (default is 0.125) max_dist: float, optional Maximum distance for convergence (default is 1e-4) max_iter: int, optional Maximum number of iterations (default is 500) 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) * filtered (niimg): The filtered image (_tv-img) * residual (niimg): The image residuals (_tv-res) Notes ---------- Original Java module by Pierre-Louis Bazin. Algorithm adapted from [1]_ References ---------- .. [1] Chambolle (2004). An Algorithm for Total Variation Minimization and Applications. doi:10.1023/B:JMIV.0000011325.36760.1e """ print('\nTotal variation filtering') # 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='tv-img')) res_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=image, suffix='tv-res')) if overwrite is False \ and os.path.isfile(out_file) and os.path.isfile(res_file) : print("skip computation (use existing results)") output = {'filtered': out_file, 'residual': res_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 algo = nighresjava.TotalVariationFiltering() # 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 algo.setDimensions(dimensions[0], dimensions[1], dimensions[2]) algo.setResolutions(resolution[0], resolution[1], resolution[2]) algo.setImage(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) if mask is not None: algo.setMaskImage(idx, nighresjava.JArray('int')( (load_volume(mask).get_data().flatten('F')).astype(int).tolist())) # set algorithm parameters algo.setLambdaScale(lambda_scale) algo.setTauStep(tau_step) algo.setMaxDist(max_dist) algo.setMaxIter(max_iter) # execute the algorithm try: algo.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 filtered_data = np.reshape(np.array(algo.getFilteredImage(), 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(filtered_data) header['cal_max'] = np.nanmax(filtered_data) out = nb.Nifti1Image(filtered_data, affine, header) # reshape output to what nibabel likes residual_data = np.reshape(np.array(algo.getResidualImage(), 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(residual_data) header['cal_max'] = np.nanmax(residual_data) res = nb.Nifti1Image(residual_data, affine, header) if save_data: save_volume(out_file, out) save_volume(res_file, res) return {'filtered': out_file, 'residual': res_file} else: return {'filtered': out, 'residual': res}