diff --git a/doc/source/user_guide/parallelization.txt b/doc/source/user_guide/parallelization.txt
index 20517de5..f0323c7d 100755
--- a/doc/source/user_guide/parallelization.txt
+++ b/doc/source/user_guide/parallelization.txt
@@ -1,13 +1,13 @@
-=========================
-How to parallelize loops?
-=========================
+========================
+How to parallelize loops
+========================
 
 In image processing, we frequently apply the same algorithm
-on a large batch of images. Let us define an example.
+on a large batch of images. Here is an example:
 
 .. code-block:: python
 
-    from skimage import data, color
+    from skimage import data, color, util
     from skimage.restoration import denoise_tv_chambolle
     from skimage.feature import hog
 
@@ -25,24 +25,18 @@ on a large batch of images. Let us define an example.
     # Prepare images
     hubble = data.hubble_deep_field()
     width = 10
-    pics = [hubble[:,slice:slice+width] for slice in range(0, 1000, width)]
+    pics = util.view_as_windows(hubble, (width, hubble.shape[1], hubble.shape[2]), step=width)
 
 To call the function ``task`` on each element of the list ``pics``, it is
-usual to write a for loop. To measure the execution time of this loop, a function
-is defined and called with ``timeit``.
+usual to write a for loop. To measure the execution time of this loop, you can
+use ipython and measure the execution time with ``%timeit``.
 
 .. code-block:: python
 
     def classic_loop():
         for image in pics:
-            task(image)
+            task(image[0][0])
 
-    import timeit
-    print("classic_loop():", timeit.timeit("classic_loop()", setup="from __main__ import (classic_loop, task, pics)", number=1))
-
-Alternatively, you can use ipython and measure the execution time with ``%timeit``.
-
-.. code-block:: python
 
     %timeit classic_loop()
 
@@ -51,7 +45,7 @@ Another equivalent way to code this loop is to use a comprehension list which ha
 .. code-block:: python
 
     def comprehension_loop():
-        [task(image) for image in pics]
+        [task(image[0][0]) for image in pics]
 
     %timeit comprehension_loop()
 
@@ -62,6 +56,6 @@ The number of jobs can be specified.
 
     from joblib import Parallel, delayed
     def joblib_loop():
-        Parallel(n_jobs=4)(delayed(task)(i) for i in pics)
+        Parallel(n_jobs=4)(delayed(task)(i[0][0]) for i in pics)
 
     %timeit joblib_loop()