multiscale_vessel_filter

nighres.filtering.multiscale_vessel_filter(input_image, structure_intensity='bright', filterType='RRF', propagationtype='diffusion', threshold=0.5, factor=0.5, max_diff=0.001, max_itr=100, scale_step=1.0, scales=4, prior_image=None, invert_prior=False, save_data=False, overwrite=False, output_dir=None, file_name=None)[source]

Vessel filter with prior

Uses an image filter to make a probabilistic image of ridge structures.

Parameters:
  • input_image (niimg) – Image containing structure-of-interest
  • structure_intensity (str) – Image intensity of structure-of-interest ‘bright’, ‘dark’, or ‘both’ (default is ‘bright’).
  • filterType (str) – Decide for a filter type: either RRF or Hessian (default is ‘RRF’)
  • propagationtype (str) – Set the diffusion model of the filter: either ‘diffusion’ or ‘belief’ propagation model (default is ‘diffusion’)
  • threshold (float) – Set the propability treshold to decide at what probability the detected structure should be seen as a vessel (default is 0.5)
  • factor (float) – Diffusion factor between 0 and 100 (default is 0.5)
  • max_diff (float) – maximal difference for stopping (default is 0.001)
  • max_itr (int) – maximale iteration number (default is 100)
  • scale_step (float) – Scaling step between diameters (default is 1)
  • scales (int) – Number of scales to use (default is 4)
  • prior_image (niimg (opt)) – Image prior for the region to include (positive) or exclude (negative)
  • invert_prior (boolean, optional (default is False)) – In case there is a prior, the prior can be considered as negative prior (False) or as positive prior (True)
  • save_data (bool, optional) – 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:

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

  • segmentation: segmented vessel centerlines (_mvf-seg)
  • filtered: result of the vessel filtering step (_mvf-filter)
  • probability: probability score of segmented centerlines (_mvf-proba)
  • scale: discrete scale at which the centerlines are detected (_mvf-scale)
  • diameter: estimated vessel diameter (_mvf-dia)
  • length: lenght of continuous vessel segments (_mvf-length)
  • pv: partial volume estimate of vessels (_mvf-pv)
  • label: labeling of individual vessel segments (_mvf-label)
  • direction: estimated vessel direction (_mvf-dir)
Return type:

dict

Notes

Original Java module by Pierre-Louis Bazin and Julia Huck.

Examples using nighres.filtering.multiscale_vessel_filter