diff --git a/doc/examples/segmentation/plot_thresholding.py b/doc/examples/segmentation/plot_thresholding.py
index cae58a32..55abf889 100644
--- a/doc/examples/segmentation/plot_thresholding.py
+++ b/doc/examples/segmentation/plot_thresholding.py
@@ -6,10 +6,12 @@ Thresholding
 Thresholding is used to create a binary image from a grayscale image [1]_.
 
 Thresholding algorithms can be separated in two categories:
-    * Global. They are based on the histogram of the pixel intensity of
-    the image.
-    * Local. To process a pixel, only the neighboring pixels are used.
-    These algorithms often require more computation time.
+
+* Histogram-based. The histogram of the pixel intensity is used and
+assumptions may be made on the properties of this histogram (e.g. bimodal).
+
+* Local. To process a pixel, only the neighboring pixels are used.
+These algorithms often require more computation time.
 
 Scikit-image includes a function to test thresholding algorithms provided
 in the library. Therefore, in a glance, you can select the best algorithm
@@ -18,20 +20,26 @@ for you data, without a deep understanding of their mechanisms.
 .. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29
 
 """
+import matplotlib
+import matplotlib.pyplot as plt
+
 from skimage.data import page
+from skimage.filters import thresholding
 
 img = page()
 
 # Here, we specify a radius for local thresholding algorithm.
 # If it is not specified, only global algorithms are called.
-fig, ax = mosaic_threshold(img, radius=20, figsize=(10,8), verbose=False)
-fig
+fig, ax = thresholding.mosaic_threshold(img, radius=20,
+                                        figsize=(10,8), verbose=False)
+fig.show()
 
 """
 
 .. image:: PLOT2RST.current_figure
 
-This example uses Otsu's method [2]_ to calculate the threshold value.
+Now, we illustrate how to apply one of these thresholding algorithms
+This example uses Otsu's method [2]_.
 
 Otsu's method calculates an "optimal" threshold (marked by a red line in the
 histogram below) by maximizing the variance between two classes of pixels,
@@ -55,7 +63,6 @@ image = camera()
 thresh = threshold_otsu(image)
 binary = image > thresh
 
-#fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 2.5))
 fig = plt.figure(figsize=(8, 2.5))
 ax1 = plt.subplot(1, 3, 1, adjustable='box-forced')
 ax2 = plt.subplot(1, 3, 2)
@@ -74,3 +81,7 @@ ax3.set_title('Thresholded')
 ax3.axis('off')
 
 plt.show()
+
+"""
+.. image:: PLOT2RST.current_figure
+"""
diff --git a/skimage/filters/thresholding.py b/skimage/filters/thresholding.py
index fcc51b6f..1df0042a 100644
--- a/skimage/filters/thresholding.py
+++ b/skimage/filters/thresholding.py
@@ -62,7 +62,7 @@ def _mosaic(image, methods=None, figsize=None, num_cols=2, verbose=True):
         a.axis('off')
 
     fig.tight_layout()
-    plt.close()
+    #plt.close()
     return fig, ax