nighres.brain.intensity_based_skullstripping(main_image, extra_image=None, noise_model='exponential', skip_zero_values=True, iterate=False, dilate_mask=0, dynamic_range=0.8, topology_lut_dir=None, save_data=False, overwrite=False, output_dir=None, file_name=None)[source]

Intensity-based skull stripping

Estimate a brain mask for a dataset with good brain/background intensity separation (e.g. PD-weighted). An extra image can be used to ensure high intensities are preserved (e.g. T1 map, T2-weighted data or a probability map for a ROI).

  • main_image (niimg) – Main Intensity Image
  • extra_image (niimg, optional) – Extra image with high intensity at brain boundary
  • noise_model ({'exponential','half-normal','exp+log-normal','half+log-normal'}) – Background noise model (default is ‘exponential’)
  • skip_zero_values (bool) – Ignores voxels with zero value (default is True)
  • iterate (bool) – Whether to iterate the estimation (may be unstable in some cases, default is False)
  • dilate_mask (int) – Additional dilation (or erosion, if negative) of the brain mask (default is 0)
  • dynamic_range (float) – Dynamic range for the foreground / background differences in [0,1] (default is 0.8)
  • topology_lut_dir (str, optional) – Path to directory in which topology files are stored (default is stored in TOPOLOGY_LUT_DIR)
  • 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)

Dictionary collecting outputs under the following keys (suffix of output files in brackets)

  • brain_mask (niimg): Binary brain mask (_istrip-mask)
  • brain_proba (niimg): Probability brain map (_istrip-proba)
  • main_masked (niimg): Masked main image (_istrip-main)
  • extra_masked (niimg): Masked extra map (_istrip-extra)

Return type:



Original Java module by Pierre-Louis Bazin. Details on the algorithm can be found in [1]


[1]Bazin et al. (2014). A computational framework for ultra-high resolution cortical segmentation at 7 Tesla. DOI: 10.1016/j.neuroimage.2013.03.077