code: def clean_cc_mask(mask):
      """
      Cleans a segmentation of the corpus callosum so no random pixels
      are included.
  
      Parameters
      ----------
      mask : ndarray
          Binary mask of the coarse segmentation.
  
      Returns
      -------
      new_cc_mask : ndarray
          Binary mask of the cleaned segmentation.
      """
  
      from scipy.ndimage import label
  
      new_cc_mask = np.zeros(mask.shape)
  
      # Flood fill algorithm to find contiguous regions.
      labels, numL = label(mask)
  
      volumes = [
          len(labels[np.where(labels == l_idx + 1)])
          for l_idx in np.arange(numL)
      ]
      biggest_vol = (
          np.arange(numL)[np.where(volumes == np.max(volumes))] + 1
      )
      new_cc_mask[np.where(labels == biggest_vol)] = 1
  
      return new_cc_mask
  
file_path: dipy/dipy/segment/mask.py
imports: from warnings import warn
  import numpy
  from dipy.reconst.dti import fractional_anisotropy, color_fa
  from scipy.ndimage import median_filter
  from scipy.ndimage import binary_dilation, generate_binary_structure
label_desc: Use image node_id 7 for calling a global function with example usage: clean_cc_mask(mask) and returns: new_cc_mask