diff --git a/doc/examples/plot_otsu.py b/doc/examples/plot_otsu.py
new file mode 100644
index 00000000..f2335fc0
--- /dev/null
+++ b/doc/examples/plot_otsu.py
@@ -0,0 +1,45 @@
+"""
+============
+Thresholding
+============
+
+Thresholding is used to create a binary image. This example uses Otsu's method
+to calculate the threshold value.
+
+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,
+which are separated by the threshold. Equivalently, this threshold minimizes
+the intra-class variance.
+
+.. [1] http://en.wikipedia.org/wiki/Otsu's_method
+
+"""
+
+import matplotlib.pyplot as plt
+
+from skimage.data import camera
+from skimage.filter import threshold_otsu
+
+
+image = camera()
+thresh = threshold_otsu(image)
+binary = image > thresh
+
+plt.figure(figsize=(8, 2.5))
+plt.subplot(1, 3, 1)
+plt.imshow(image, cmap=plt.cm.gray)
+plt.title('Original')
+plt.axis('off')
+
+plt.subplot(1, 3, 2, aspect='equal')
+plt.hist(image)
+plt.title('Histogram')
+plt.axvline(thresh, color='r')
+
+plt.subplot(1, 3, 3)
+plt.imshow(binary, cmap=plt.cm.gray)
+plt.title('Thresholded')
+plt.axis('off')
+
+plt.show()
+
diff --git a/doc/examples/plot_thresholding.py b/doc/examples/plot_thresholding.py
deleted file mode 100644
index ada0552e..00000000
--- a/doc/examples/plot_thresholding.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""
-============
-Thresholding
-============
-
-Thresholding is used to create a binary image.
-
-This example uses Otsu's method to calculate the threshold value. 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, which are separated by
-the threshold. Equivalently, this threshold minimizes the intra-class variance.
-
-Additionally an adaptive thresholding is applied. Also known as local or
-dynamic thresholding where the threshold value is the weighted mean for the
-local neighborhood of a pixel subtracted by a constant. Small filter block sizes
-are suitable for thresholding edges, large filter block sizes suitable for
-thresholding larger homogeneous regions.
-
-.. [1] http://en.wikipedia.org/wiki/Otsu's_method
-
-"""
-
-import matplotlib.pyplot as plt
-
-from skimage.data import camera
-from skimage.filter import threshold_otsu, threshold_adaptive
-
-
-image = camera()
-
-
-#: Otsu thresholding
-thresh = threshold_otsu(image)
-otsu_binary = image > thresh
-
-plt.figure(figsize=(8, 6))
-plt.subplot(2, 3, 1)
-plt.imshow(image, cmap=plt.cm.gray)
-plt.title('Original')
-plt.axis('off')
-
-plt.subplot(2, 3, 2, aspect='equal')
-plt.hist(image)
-plt.title('Histogram')
-plt.axvline(thresh, color='r')
-
-plt.subplot(2, 3, 3)
-plt.imshow(otsu_binary, cmap=plt.cm.gray)
-plt.title('Thresholded with Otsu')
-plt.axis('off')
-
-
-#: Adaptive thresholding
-plt.subplot(2, 3, 4)
-plt.imshow(threshold_adaptive(image, 11, method='gaussian', offset=5),
-    cmap=plt.cm.gray)
-plt.title('Adaptive edge thresholding')
-plt.axis('off')
-
-plt.subplot(2, 3, 5)
-plt.imshow(threshold_adaptive(image, 125, method='gaussian', offset=7.5),
-    cmap=plt.cm.gray)
-plt.title('Adaptive Gaussian')
-plt.axis('off')
-
-plt.subplot(2, 3, 6)
-plt.imshow(threshold_adaptive(image, 125, method='mean', offset=7.5),
-    cmap=plt.cm.gray)
-plt.title('Adaptive Mean')
-plt.axis('off')
-
-plt.show()