code: def label_clusters(image, min_cluster_size=50, min_thresh=1e-6, max_thresh=1, fully_connected=False):
      
      dim = image.dimension
      clust = threshold_image(image, min_thresh, max_thresh)
      temp = int(fully_connected)
      args = [dim, clust, clust, min_cluster_size, temp]
      processed_args = _int_antsProcessArguments(args)
      libfn = utils.get_lib_fn('LabelClustersUniquely')
      libfn(processed_args)
      return clust
docs: This will give a unique ID to each connected 
      component 1 through N of size > min_cluster_size
  
      ANTsR function: `labelClusters`
  
      Arguments
      ---------
      image : ANTsImage 
          input image e.g. a statistical map
      
      min_cluster_size : integer  
          throw away clusters smaller than this value
      
      min_thresh : scalar
          threshold to a statistical map
      
      max_thresh : scalar
          threshold to a statistical map
      
      fully_connected : boolean
          boolean sets neighborhood connectivity pattern
      
      Returns
      -------
      ANTsImage
  
      Example
      -------
      >>> import ants
      >>> image = ants.image_read( ants.get_ants_data('r16') )
      >>> timageFully = ants.label_clusters( image, 10, 128, 150, True )
      >>> timageFace = ants.label_clusters( image, 10, 128, 150, False )