diff --git a/doc/examples/plot_entropy.py b/doc/examples/plot_entropy.py
index 39e2a740..363c88ed 100644
--- a/doc/examples/plot_entropy.py
+++ b/doc/examples/plot_entropy.py
@@ -7,22 +7,17 @@ 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.
+neighborhood, typically defined by a structuring element. The entropy filter can
+detect subtle variations in the local gray level distribution.
 
-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.
+In the first 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.
+To detect the central square, we compute the local entropy measure using a
+circular structuring element of a radius big enough to capture the local gray
+level distribution. The second example shows how to detect texture in the camera
+image using a smaller structuring element.
 
 """
 import matplotlib.pyplot as plt
@@ -33,40 +28,47 @@ from skimage.util import img_as_ubyte
 from skimage.filters.rank import entropy
 from skimage.morphology import disk
 
+# First example: object detection.
+
 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 = (noise_mask * np.random.random(noise_mask.shape) - 0.5 *
          noise_mask).astype(np.uint8)
 img = noise + 128
 
-radius = 10
-e = entropy(img, disk(radius))
+entr_img = entropy(img, disk(10))
 
-fig, ax = plt.subplots(1, 3, figsize=(8, 5))
-ax1, ax2, ax3 = ax.ravel()
+fig, ax = plt.subplots(1, 3, figsize=(8, 3))
+ax0, ax1, ax2 = 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)
+ax0.imshow(noise_mask, cmap=plt.cm.gray)
+ax0.set_xlabel("Noise mask")
+ax1.imshow(img, cmap=plt.cm.gray)
+ax1.set_xlabel("Noisy image")
+ax2.imshow(entr_img)
+ax2.set_xlabel("Local entropy")
 
-# second example: texture detection
+fig.tight_layout()
+
+# Second example: texture detection.
 
 image = img_as_ubyte(data.camera())
 
-fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(10, 4), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
+fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(10, 4), sharex=True,
+                               sharey=True,
+                               subplot_kw={"adjustable": "box-forced"})
 
 img0 = ax0.imshow(image, cmap=plt.cm.gray)
-ax0.set_title('Image')
-ax0.axis('off')
+ax0.set_title("Image")
+ax0.axis("off")
 fig.colorbar(img0, ax=ax0)
 
 img1 = ax1.imshow(entropy(image, disk(5)), cmap=plt.cm.jet)
-ax1.set_title('Entropy')
-ax1.axis('off')
+ax1.set_title("Entropy")
+ax1.axis("off")
 fig.colorbar(img1, ax=ax1)
 
+fig.tight_layout()
+
 plt.show()