diff --git a/skimage/filter/rank/bilateral_rank.pyx b/skimage/filter/rank/bilateral_rank.pyx
index 0ec299b0..818f0c64 100644
--- a/skimage/filter/rank/bilateral_rank.pyx
+++ b/skimage/filter/rank/bilateral_rank.pyx
@@ -170,11 +170,11 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False,
     >>> # Local mean
     >>> from skimage.morphology import square
     >>> import skimage.filter.rank as rank
-    >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
-    ...                           [0, 1, 1, 1, 0],
-    ...                           [0, 1, 1, 1, 0],
-    ...                           [0, 1, 1, 1, 0],
-    ...                           [0, 0, 0, 0, 0]], dtype=np.uint8)
+    >>> ima8 = 255 * np.array([[0, 0, 0, 0, 0],
+    ...                        [0, 1, 1, 1, 0],
+    ...                        [0, 1, 1, 1, 0],
+    ...                        [0, 1, 1, 1, 0],
+    ...                        [0, 0, 0, 0, 0]], dtype=np.uint8)
     >>> rank.bilateral_pop(ima8, square(3), s0=10,s1=10)
     array([[3, 4, 3, 4, 3],
            [4, 4, 6, 4, 4],
diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx
index 02720216..c1878f34 100644
--- a/skimage/filter/rank/rank.pyx
+++ b/skimage/filter/rank/rank.pyx
@@ -521,11 +521,11 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     >>> # Local mean
     >>> from skimage.morphology import square
     >>> import skimage.filter.rank as rank
-    >>> ima = 255*np.array([[0, 0, 0, 0, 0],
-    ...                           [0, 1, 1, 1, 0],
-    ...                           [0, 1, 1, 1, 0],
-    ...                           [0, 1, 1, 1, 0],
-    ...                           [0, 0, 0, 0, 0]], dtype=np.uint8)
+    >>> ima = 255 * np.array([[0, 0, 0, 0, 0],
+    ...                       [0, 1, 1, 1, 0],
+    ...                       [0, 1, 1, 1, 0],
+    ...                       [0, 1, 1, 1, 0],
+    ...                       [0, 0, 0, 0, 0]], dtype=np.uint8)
     >>> rank.pop(ima, square(3))
     array([[4, 6, 6, 6, 4],
            [6, 9, 9, 9, 6],
@@ -570,11 +570,11 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     >>> # Local threshold
     >>> from skimage.morphology import square
     >>> from skimage.filter.rank import threshold
-    >>> ima = 255*np.array([[0, 0, 0, 0, 0],
-    ...                           [0, 1, 1, 1, 0],
-    ...                           [0, 1, 1, 1, 0],
-    ...                           [0, 1, 1, 1, 0],
-    ...                           [0, 0, 0, 0, 0]], dtype=np.uint8)
+    >>> ima = 255 * np.array([[0, 0, 0, 0, 0],
+    ...                       [0, 1, 1, 1, 0],
+    ...                       [0, 1, 1, 1, 0],
+    ...                       [0, 1, 1, 1, 0],
+    ...                       [0, 0, 0, 0, 0]], dtype=np.uint8)
     >>> threshold(ima, square(3))
     array([[0, 0, 0, 0, 0],
            [0, 1, 1, 1, 0],
@@ -703,11 +703,11 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     >>> from skimage.morphology import disk
     >>> # defining a 8- and a 16-bit test images
     >>> a8 = data.camera()
-    >>> a16 = data.camera().astype(np.uint16)*4
+    >>> a16 = data.camera().astype(np.uint16) * 4
     >>> # pixel values contain 10x the local entropy
-    >>> ent8 = entropy(a8,disk(5))
+    >>> ent8 = entropy(a8, disk(5))
     >>> # pixel values contain 1000x the local entropy
-    >>> ent16 = entropy(a16,disk(5))
+    >>> ent16 = entropy(a16, disk(5))
 
     """
 
@@ -752,7 +752,7 @@ def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     >>> from skimage.morphology import disk
     >>> # defining a 8- and a 16-bit test images
     >>> a8 = data.camera()
-    >>> loc_otsu = otsu(a8,disk(5))
+    >>> loc_otsu = otsu(a8, disk(5))
     >>> thresh_image = a8 >= loc_otsu
 
     """