diff --git a/skimage/filter/rank/bilateral_rank.pyx b/skimage/filter/rank/bilateral_rank.pyx
index 0d3cdd88..dec07c28 100644
--- a/skimage/filter/rank/bilateral_rank.pyx
+++ b/skimage/filter/rank/bilateral_rank.pyx
@@ -1,11 +1,7 @@
 """Approximate bilateral rank filter for local (custom kernel) mean.
 
 The local histogram is computed using a sliding window similar to the method
-described in:
-
-.. [1] Reference: Huang, T. ,Yang, G. ;  Tang, G.. "A fast two-dimensional
-       median filtering algorithm", IEEE Transactions on Acoustics, Speech and
-       Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18.
+described in [1]_.
 
 Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), 8-bit
 images are casted in 16-bit the number of histogram bins is determined from the
@@ -21,6 +17,13 @@ equally).
 
 Result image is 16-bit with respect to the input image.
 
+References
+----------
+
+.. [1] Huang, T. ,Yang, G. ;  Tang, G.. "A fast two-dimensional
+       median filtering algorithm", IEEE Transactions on Acoustics, Speech and
+       Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18.
+
 """
 
 import numpy as np
diff --git a/skimage/filter/rank/percentile_rank.pyx b/skimage/filter/rank/percentile_rank.pyx
index 62cc4576..7deae623 100644
--- a/skimage/filter/rank/percentile_rank.pyx
+++ b/skimage/filter/rank/percentile_rank.pyx
@@ -5,14 +5,7 @@ instead of using [min, max]. It means that isolated bright or dark pixels will
 not produce halos.
 
 The local histogram is computed using a sliding window similar to the method
-described in [1].
-
-References
-==========
-
-.. [1] Huang, T. ,Yang, G. ;  Tang, G.. "A fast two-dimensional
-       median filtering algorithm", IEEE Transactions on Acoustics, Speech and
-       Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18.
+described in [1]_.
 
 Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), for 16-bit
 input images, the number of histogram bins is determined from the maximum value
@@ -20,6 +13,13 @@ present in the image.
 
 Result image is 8 or 16-bit with respect to the input image.
 
+References
+----------
+
+.. [1] Huang, T. ,Yang, G. ;  Tang, G.. "A fast two-dimensional
+       median filtering algorithm", IEEE Transactions on Acoustics, Speech and
+       Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18.
+
 """
 
 import numpy as np
diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx
index be078ddd..43f03133 100644
--- a/skimage/filter/rank/rank.pyx
+++ b/skimage/filter/rank/rank.pyx
@@ -1,9 +1,5 @@
 """The local histogram is computed using a sliding window similar to the method
-described in:
-
-.. [1] Reference: Huang, T. ,Yang, G. ;  Tang, G.. "A fast two-dimensional
-       median filtering algorithm", IEEE Transactions on Acoustics, Speech and
-       Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18.
+described in [1]_.
 
 Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), for 16-bit
 input images, the number of histogram bins is determined from the maximum value
@@ -11,6 +7,13 @@ present in the image.
 
 Result image is 8 or 16-bit with respect to the input image.
 
+References
+----------
+
+.. [1] Huang, T. ,Yang, G. ;  Tang, G.. "A fast two-dimensional
+       median filtering algorithm", IEEE Transactions on Acoustics, Speech and
+       Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18.
+
 """
 
 import numpy as np