diff --git a/gklearn/preimage/median_preimage_generator.py b/gklearn/preimage/median_preimage_generator.py
index f342465..ead6a9a 100644
--- a/gklearn/preimage/median_preimage_generator.py
+++ b/gklearn/preimage/median_preimage_generator.py
@@ -18,6 +18,7 @@ from gklearn.ged.median import MedianGraphEstimator
 from gklearn.ged.median import constant_node_costs,mge_options_to_string
 from gklearn.gedlib import librariesImport, gedlibpy
 from gklearn.utils import Timer
+from gklearn.utils.utils import get_graph_kernel_by_name
 # from gklearn.utils.dataset import Dataset
 
 class MedianPreimageGenerator(PreimageGenerator):
@@ -81,7 +82,13 @@ class MedianPreimageGenerator(PreimageGenerator):
 		
 		
 	def run(self):
-		self.__set_graph_kernel_by_name()
+		self._graph_kernel = get_graph_kernel_by_name(self._kernel_options['name'], 
+						  node_labels=self._dataset.node_labels,
+						  edge_labels=self._dataset.edge_labels, 
+						  node_attrs=self._dataset.node_attrs,
+						  edge_attrs=self._dataset.edge_attrs,
+						  ds_infos=self._dataset.get_dataset_infos(keys=['directed']),
+						  **self._kernel_options)
 		
 		# record start time.
 		start = time.time()
@@ -722,43 +729,6 @@ class MedianPreimageGenerator(PreimageGenerator):
 			print('distance in kernel space for generalized median:', self.__k_dis_gen_median)
 			print('minimum distance in kernel space for each graph in median set:', self.__k_dis_dataset)
 			print('distance in kernel space for each graph in median set:', k_dis_median_set)	
-		
-
-	def __set_graph_kernel_by_name(self):
-		if self._kernel_options['name'] == 'ShortestPath':
-			from gklearn.kernels import ShortestPath
-			self._graph_kernel = ShortestPath(node_labels=self._dataset.node_labels,
-									 node_attrs=self._dataset.node_attrs,
-									 ds_infos=self._dataset.get_dataset_infos(keys=['directed']),
-									 **self._kernel_options)
-		elif self._kernel_options['name'] == 'StructuralSP':
-			from gklearn.kernels import StructuralSP
-			self._graph_kernel = StructuralSP(node_labels=self._dataset.node_labels,
-									  edge_labels=self._dataset.edge_labels, 
-									  node_attrs=self._dataset.node_attrs,
-									  edge_attrs=self._dataset.edge_attrs,
-									  ds_infos=self._dataset.get_dataset_infos(keys=['directed']),
-									  **self._kernel_options)
-		elif self._kernel_options['name'] == 'PathUpToH':
-			from gklearn.kernels import PathUpToH
-			self._graph_kernel = PathUpToH(node_labels=self._dataset.node_labels,
-								  edge_labels=self._dataset.edge_labels,
-								  ds_infos=self._dataset.get_dataset_infos(keys=['directed']),
-								  **self._kernel_options)
-		elif self._kernel_options['name'] == 'Treelet':
-			from gklearn.kernels import Treelet
-			self._graph_kernel = Treelet(node_labels=self._dataset.node_labels,
-								  edge_labels=self._dataset.edge_labels,
-								  ds_infos=self._dataset.get_dataset_infos(keys=['directed']),
-								  **self._kernel_options)
-		elif self._kernel_options['name'] == 'WeisfeilerLehman':
-			from gklearn.kernels import WeisfeilerLehman
-			self._graph_kernel = WeisfeilerLehman(node_labels=self._dataset.node_labels,
-								  edge_labels=self._dataset.edge_labels,
-								  ds_infos=self._dataset.get_dataset_infos(keys=['directed']),
-								  **self._kernel_options)
-		else:
-			raise Exception('The graph kernel given is not defined. Possible choices include: "StructuralSP", "ShortestPath", "PathUpToH", "Treelet", "WeisfeilerLehman".')
 			
 			
 # 	def __clean_graph(self, G, node_labels=[], edge_labels=[], node_attrs=[], edge_attrs=[]):