code: def segment_from_cfa(tensor_fit, roi, threshold, return_cfa=False):
      """
      Segment the cfa inside roi using the values from threshold as bounds.
  
      Parameters
      ----------
      tensor_fit : TensorFit object
          TensorFit object
  
      roi : ndarray
          A binary mask, which contains the bounding box for the segmentation.
  
      threshold : array-like
          An iterable that defines the min and max values to use for the
          thresholding.
          The values are specified as (R_min, R_max, G_min, G_max, B_min, B_max)
  
      return_cfa : bool, optional
          If True, the cfa is also returned.
  
      Returns
      -------
      mask : ndarray
          Binary mask of the segmentation.
  
      cfa : ndarray, optional
          Array with shape = (..., 3), where ... is the shape of tensor_fit.
          The color fractional anisotropy, ordered as a nd array with the last
          dimension of size 3 for the R, G and B channels.
      """
  
      FA = fractional_anisotropy(tensor_fit.evals)
      FA[np.isnan(FA)] = 0
      FA = np.clip(
          FA, 0, 1
      )  # Clamp the FA to remove degenerate tensors
  
      cfa = color_fa(FA, tensor_fit.evecs)
      roi = np.asarray(roi, dtype=bool)
  
      include = (
          (cfa >= threshold[0::2])
          & (cfa <= threshold[1::2])
          & roi[..., None]
      )
      mask = np.all(include, axis=-1)
  
      if return_cfa:
          return mask, cfa
  
      return 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 6 for calling a global function with example usage: segment_from_cfa(tensor_fit, roi, threshold, return_cfa) and returns: mask, mask,  cfa