diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx
index e8a4c8f1..5380363a 100644
--- a/skimage/filter/rank/rank.pyx
+++ b/skimage/filter/rank/rank.pyx
@@ -669,7 +669,7 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False,
 
 
 def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
-    """Returns the entropy [wiki_entropy]_ computed locally. Entropy is computed
+    """Returns the entropy [1]_ computed locally. Entropy is computed
     using base 2 logarithm i.e. the filter returns the minimum number of
     bits needed to encode local greylevel distribution.
 
@@ -698,7 +698,7 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
 
     References
     ----------
-    .. [wiki_entropy] http://en.wikipedia.org/wiki/Entropy_(information_theory)
+    .. [1] http://en.wikipedia.org/wiki/Entropy_(information_theory)
 
     Examples
     --------
diff --git a/skimage/io/collection.py b/skimage/io/collection.py
index 5a5dfff6..fafca83f 100644
--- a/skimage/io/collection.py
+++ b/skimage/io/collection.py
@@ -102,9 +102,6 @@ class MultiImage(object):
     (15, 10)
     (15, 10)
 
-    The two frames in this image can be shown with matplotlib:
-
-    .. plot:: show_collection.py
     """
     def __init__(self, filename, conserve_memory=True, dtype=None):
         """Load a multi-img."""
diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py
index e83cecd5..1552e531 100644
--- a/skimage/transform/hough_transform.py
+++ b/skimage/transform/hough_transform.py
@@ -140,8 +140,6 @@ def hough_line(img, theta=None):
 
     >>> out, angles, d = hough(img)
 
-    .. plot:: hough_tf.py
-
     """
     return _hough(img, theta)