diff --git a/doc/examples/plot_phase_unwrap.py b/doc/examples/plot_phase_unwrap.py
index ded87ada..f2bbbc4c 100644
--- a/doc/examples/plot_phase_unwrap.py
+++ b/doc/examples/plot_phase_unwrap.py
@@ -92,7 +92,7 @@ is clear: Without unwrapping (lower left), the regions above and below the
 masked boundary do not interact at all, resulting in an offset between the
 two regions of an arbitrary integer times two pi. We could just as well have
 unwrapped the regions as two separate images. With wrap around enabled for the
-vertical direction (lower rigth), the situation changes: Unwrapping paths are
+vertical direction (lower right), the situation changes: Unwrapping paths are
 now allowed to pass from the bottom to the top of the image and vice versa, in
 effect providing a way to determine the offset between the two regions.
 
diff --git a/doc/examples/plot_rag.py b/doc/examples/plot_rag.py
index f360af52..e44f4094 100644
--- a/doc/examples/plot_rag.py
+++ b/doc/examples/plot_rag.py
@@ -31,7 +31,7 @@ def max_edge(g, src, dst, n):
     g : RAG
         The graph under consideration.
     src, dst : int
-        The verices in `g` to be merged.
+        The vertices in `g` to be merged.
     n : int
         A neighbor of `src` or `dst` or both.
 
diff --git a/skimage/_shared/testing.py b/skimage/_shared/testing.py
index f8c7a88e..0cf65cb4 100644
--- a/skimage/_shared/testing.py
+++ b/skimage/_shared/testing.py
@@ -48,7 +48,7 @@ def doctest_skip_parser(func):
         >>> something + else
         >>> something # skip if HAVE_BMODULE
 
-    This decorator will evaluate the expresssion after ``skip if``.  If this
+    This decorator will evaluate the expression after ``skip if``.  If this
     evaluates to True, then the comment is replaced by ``# doctest: +SKIP``.  If
     False, then the comment is just removed. The expression is evaluated in the
     ``globals`` scope of `func`.
diff --git a/skimage/color/colorconv.py b/skimage/color/colorconv.py
index 959fad3e..8e8c3b41 100644
--- a/skimage/color/colorconv.py
+++ b/skimage/color/colorconv.py
@@ -1350,7 +1350,7 @@ def lab2lch(lab):
 
 
 def _cart2polar_2pi(x, y):
-    """convert cartesian coordiantes to polar (uses non-standard theta range!)
+    """convert cartesian coordinates to polar (uses non-standard theta range!)
 
     NON-STANDARD RANGE! Maps to ``(0, 2*pi)`` rather than usual ``(-pi, +pi)``
     """
diff --git a/skimage/feature/texture.py b/skimage/feature/texture.py
index b1e1024a..45d97b97 100644
--- a/skimage/feature/texture.py
+++ b/skimage/feature/texture.py
@@ -11,7 +11,7 @@ def greycomatrix(image, distances, angles, levels=256, symmetric=False,
                  normed=False):
     """Calculate the grey-level co-occurrence matrix.
 
-    A grey level co-occurence matrix is a histogram of co-occuring
+    A grey level co-occurrence matrix is a histogram of co-occurring
     greyscale values at a given offset over an image.
 
     Parameters
diff --git a/skimage/graph/_ncut.py b/skimage/graph/_ncut.py
index 2735bbd2..05be8cd1 100644
--- a/skimage/graph/_ncut.py
+++ b/skimage/graph/_ncut.py
@@ -20,7 +20,7 @@ def DW_matrices(graph):
     -------
     D : csc_matrix
         The diagonal matrix of the graph. ``D[i, i]`` is the sum of weights of
-        all edges incident on `i`. All other enteries are `0`.
+        all edges incident on `i`. All other entries are `0`.
     W : csc_matrix
         The weight matrix of the graph. ``W[i, j]`` is the weight of the edge
         joining `i` to `j`.
@@ -38,7 +38,7 @@ def ncut_cost(cut, D, W):
     Parameters
     ----------
     cut : ndarray
-        The mask for the nodes in the graph. Nodes corressponding to a `True`
+        The mask for the nodes in the graph. Nodes corresponding to a `True`
         value are in one set.
     D : csc_matrix
         The diagonal matrix of the graph.
diff --git a/skimage/graph/graph_cut.py b/skimage/graph/graph_cut.py
index b14b6a34..b4658bd1 100644
--- a/skimage/graph/graph_cut.py
+++ b/skimage/graph/graph_cut.py
@@ -10,10 +10,10 @@ from scipy.sparse import linalg
 
 
 def cut_threshold(labels, rag, thresh, in_place=True):
-    """Combine regions seperated by weight less than threshold.
+    """Combine regions separated by weight less than threshold.
 
     Given an image's labels and its RAG, output new labels by
-    combining regions whose nodes are seperated by a weight less
+    combining regions whose nodes are separated by a weight less
     than the given threshold.
 
     Parameters
@@ -228,7 +228,7 @@ def _ncut_relabel(rag, thresh, num_cuts):
     """Perform Normalized Graph cut on the Region Adjacency Graph.
 
     Recursively partition the graph into 2, until further subdivision
-    yields a cut greather than `thresh` or such a cut cannot be computed.
+    yields a cut greater than `thresh` or such a cut cannot be computed.
     For such a subgraph, indices to labels of all its nodes map to a single
     unique value.
 
diff --git a/skimage/graph/heap.pyx b/skimage/graph/heap.pyx
index dde8bd60..a4368595 100644
--- a/skimage/graph/heap.pyx
+++ b/skimage/graph/heap.pyx
@@ -711,7 +711,7 @@ cdef class FastUpdateBinaryHeap(BinaryHeap):
         Returns
         -------
         pushed : bool
-            True if an append/update occured, False if otherwise.
+            True if an append/update occurred, False if otherwise.
 
         Raises
         ------
diff --git a/skimage/graph/rag.py b/skimage/graph/rag.py
index 5950dfdc..5f148c5e 100644
--- a/skimage/graph/rag.py
+++ b/skimage/graph/rag.py
@@ -26,7 +26,7 @@ def min_weight(graph, src, dst, n):
     """Callback to handle merging nodes by choosing minimum weight.
 
     Returns either the weight between (`src`, `n`) or (`dst`, `n`)
-    in `graph` or the minumum of the two when both exist.
+    in `graph` or the minimum of the two when both exist.
 
     Parameters
     ----------
@@ -41,7 +41,7 @@ def min_weight(graph, src, dst, n):
     -------
     weight : float
         The weight between (`src`, `n`) or (`dst`, `n`) in `graph` or the
-        minumum of the two when both exist.
+        minimum of the two when both exist.
 
     """
 
@@ -200,7 +200,7 @@ def rag_mean_color(image, labels, connectivity=2, mode='distance',
     """Compute the Region Adjacency Graph using mean colors.
 
     Given an image and its initial segmentation, this method constructs the
-    corresponsing Region Adjacency Graph (RAG). Each node in the RAG
+    corresponding Region Adjacency Graph (RAG). Each node in the RAG
     represents a set of pixels within `image` with the same label in `labels`.
     The weight between two adjacent regions represents how similar or
     dissimilar two regions are depending on the `mode` parameter.
diff --git a/skimage/measure/_structural_similarity.py b/skimage/measure/_structural_similarity.py
index 01e4e9eb..d3150667 100644
--- a/skimage/measure/_structural_similarity.py
+++ b/skimage/measure/_structural_similarity.py
@@ -29,7 +29,7 @@ def structural_similarity(X, Y, win_size=7,
     Returns
     -------
     s : float
-        Strucutural similarity.
+        Structural similarity.
     grad : (N * N,) ndarray
         Gradient of the structural similarity index between X and Y.
         This is only returned if `gradient` is set to True.
diff --git a/skimage/restoration/uft.py b/skimage/restoration/uft.py
index 046c981c..dfece24f 100644
--- a/skimage/restoration/uft.py
+++ b/skimage/restoration/uft.py
@@ -10,7 +10,7 @@ frequency is equal to
 
 .. math::  \frac{1}{\sqrt{n}} \sum_i x_i
 
-so the Fourier tranform has the same energy as the original image
+so the Fourier transform has the same energy as the original image
 (see ``image_quad_norm`` function). The transform is applied from the
 last axis for performance (assuming a C-order array input).
 
@@ -353,18 +353,18 @@ def ir2tf(imp_resp, shape, dim=None, is_real=True):
         The impulse responses.
     shape : tuple of int
         A tuple of integer corresponding to the target shape of the
-        tranfer function.
+        transfer function.
     dim : int, optional
         The last axis along which to compute the transform. All
         axes by default.
-    is_real : boolean (optionnal, default True)
+    is_real : boolean (optional, default True)
        If True, imp_resp is supposed real and the Hermitian property
        is used with rfftn Fourier transform.
 
     Returns
     -------
     y : complex ndarray
-       The tranfer function of shape ``shape``.
+       The transfer function of shape ``shape``.
 
     See Also
     --------
@@ -382,7 +382,7 @@ def ir2tf(imp_resp, shape, dim=None, is_real=True):
     Notes
     -----
     The input array can be composed of multiple-dimensional IR with
-    an arbitrary number of IR. The individual IR must be accesed
+    an arbitrary number of IR. The individual IR must be accessed
     through the first axes. The last ``dim`` axes contain the space
     definition.
     """