diff --git a/doc/examples/plot_entropy.py b/doc/examples/plot_entropy.py
index 3ca5c94e..39e2a740 100644
--- a/doc/examples/plot_entropy.py
+++ b/doc/examples/plot_entropy.py
@@ -3,17 +3,57 @@
 Entropy
 =======
 
-Image entropy is a quantity which is used to describe the amount of information
-coded in an image.
+In information theory, information entropy is the log-base-2 of the number of
+possible outcomes for a message.
+
+For an image, local entropy is related to the complexity contained in a given
+neighborhood, typically defined by a structuring element.
+
+The entropy filter can detect subtle variations in the local gray level
+distribution.
+In the example, the image is composed of two surfaces with two slightly
+different distributions.
+
+The image has a uniform random distribution in the range [-14, +14] in the
+middle of the image and a uniform random distribution in the range [-15, 15]
+at the image borders, both centered at a gray value of 128.
+
+We apply the local entropy measure using a circular structuring element of
+radius 10. As a result, one can detect the central square. The radius is
+big enough to efficiently sample the local gray level distribution.
+
+In the second example, the local entropy is used to detect image texture.
 
 """
 import matplotlib.pyplot as plt
+import numpy as np
 
 from skimage import data
+from skimage.util import img_as_ubyte
 from skimage.filters.rank import entropy
 from skimage.morphology import disk
-from skimage.util import img_as_ubyte
 
+noise_mask = 28 * np.ones((128, 128), dtype=np.uint8)
+noise_mask[32:-32, 32:-32] = 30
+
+noise = (noise_mask * np.random.random(noise_mask.shape) - .5 *
+         noise_mask).astype(np.uint8)
+img = noise + 128
+
+radius = 10
+e = entropy(img, disk(radius))
+
+fig, ax = plt.subplots(1, 3, figsize=(8, 5))
+ax1, ax2, ax3 = ax.ravel()
+
+ax1.imshow(noise_mask, cmap=plt.cm.gray)
+ax1.set_xlabel('Noise mask')
+ax2.imshow(img, cmap=plt.cm.gray)
+ax2.set_xlabel('Noised image')
+ax3.imshow(e)
+ax3.set_xlabel('Local entropy ($r=%d$)' % radius)
+
+# second example: texture detection
 
 image = img_as_ubyte(data.camera())