diff --git a/skimage/util/_regular_grid.py b/skimage/util/_regular_grid.py
index e304be20..898a4aed 100644
--- a/skimage/util/_regular_grid.py
+++ b/skimage/util/_regular_grid.py
@@ -3,7 +3,7 @@ import numpy as np
 
 def regular_grid(ar_shape, n_points):
     """Find `n_points` regularly spaced along `ar_shape`.
-    
+
     The returned points (as slices) should be as close to cubically-spaced as
     possible. Essentially, the points are spaced by the Nth root of the input
     array size, where N is the number of dimensions. However, if an array
@@ -13,7 +13,7 @@ def regular_grid(ar_shape, n_points):
     Parameters
     ----------
     ar_shape : array-like of ints
-        The shape of the space embedding the grid. `len(ar_shape)` is the
+        The shape of the space embedding the grid. ``len(ar_shape)`` is the
         number of dimensions.
     n_points : int
         The (approximate) number of points to embed in the space.
@@ -66,7 +66,7 @@ def regular_grid(ar_shape, n_points):
                 break
     starts = stepsizes // 2
     stepsizes = np.round(stepsizes)
-    slices = [slice(start, None, step) for 
+    slices = [slice(start, None, step) for
               start, step in zip(starts, stepsizes)]
     slices = [slices[i] for i in unsort_dim_idxs]
     return slices
diff --git a/skimage/util/montage.py b/skimage/util/montage.py
index bdafe6ed..805b78a8 100644
--- a/skimage/util/montage.py
+++ b/skimage/util/montage.py
@@ -10,7 +10,7 @@ def montage2d(arr_in, fill='mean', rescale_intensity=False, grid_shape=None):
     """Create a 2-dimensional 'montage' from a 3-dimensional input array
     representing an ensemble of equally shaped 2-dimensional images.
 
-    For example, montage2d(arr_in, fill) with the following `arr_in`
+    For example, ``montage2d(arr_in, fill)`` with the following `arr_in`
 
     +---+---+---+
     | 1 | 2 | 3 |
@@ -37,7 +37,8 @@ def montage2d(arr_in, fill='mean', rescale_intensity=False, grid_shape=None):
     rescale_intensity: bool, optional
         Whether to rescale the intensity of each image to [0, 1].
     grid_shape: tuple, optional
-        The desired grid shape for the montage (tiles_y, tiles_x). Tthe default aspect ratio is square.
+        The desired grid shape for the montage (tiles_y, tiles_x).
+        The default aspect ratio is square.
 
     Returns
     -------
diff --git a/skimage/util/shape.py b/skimage/util/shape.py
index 1d606b42..5fe27a36 100644
--- a/skimage/util/shape.py
+++ b/skimage/util/shape.py
@@ -208,11 +208,11 @@ def view_as_windows(arr_in, window_shape, step=1):
 
     # -- basic checks on arguments
     if not isinstance(arr_in, np.ndarray):
-        raise TypeError("'arr_in' must be a numpy ndarray")
+        raise TypeError("`arr_in` must be a numpy ndarray")
     if not isinstance(window_shape, tuple):
-        raise TypeError("'window_shape' must be a tuple")
+        raise TypeError("`window_shape` must be a tuple")
     if not (len(window_shape) == arr_in.ndim):
-        raise ValueError("'window_shape' is incompatible with 'arr_in.shape'")
+        raise ValueError("`window_shape` is incompatible with `arr_in.shape`")
 
     if step < 1:
         raise ValueError("`step` must be >= 1")
@@ -221,10 +221,10 @@ def view_as_windows(arr_in, window_shape, step=1):
     window_shape = np.array(window_shape, dtype=arr_shape.dtype)
 
     if ((arr_shape - window_shape) < 0).any():
-        raise ValueError("'window_shape' is too large")
+        raise ValueError("`window_shape` is too large")
 
     if ((window_shape - 1) < 0).any():
-        raise ValueError("'window_shape' is too small")
+        raise ValueError("`window_shape` is too small")
 
     # -- build rolling window view
     arr_in = np.ascontiguousarray(arr_in)
diff --git a/skimage/util/unique.py b/skimage/util/unique.py
index e65c05ce..16a83f6b 100644
--- a/skimage/util/unique.py
+++ b/skimage/util/unique.py
@@ -9,12 +9,12 @@ def unique_rows(ar):
 
     Parameters
     ----------
-    ar : 2D np.ndarray
+    ar : 2-D ndarray
         The input array.
 
     Returns
     -------
-    ar_out : 2D np.ndarray
+    ar_out : 2-D ndarray
         A copy of the input array with repeated rows removed.
 
     Raises