diff --git a/skimage/segmentation/_felzenszwalb.pyx b/skimage/segmentation/_felzenszwalb.pyx
index f4069274..15b528ad 100644
--- a/skimage/segmentation/_felzenszwalb.pyx
+++ b/skimage/segmentation/_felzenszwalb.pyx
@@ -11,21 +11,17 @@ def _felzenszwalb_segmentation_grey(image, scale=1, sigma=0.8, min_size=20):
     """Felzenszwalb's efficient graph based segmentation for a single channel.
 
     Produces an oversegmentation of a 2d image using a fast, minimum spanning
-    tree based clustering on the image grid.  The parameter ``scale`` sets an
-    observation level. Higher scale means less and larger segments. ``sigma``
-    is the diameter of a Gaussian kernel, used for smoothing the image prior to
-    segmentation.
-
+    tree based clustering on the image grid. 
     The number of produced segments as well as their size can only be
     controlled indirectly through ``scale``. Segment size within an image can
     vary greatly depending on local contrast.
 
     Parameters
     ----------
-    image: (width, height) ndarray
+    image: ndarray
         Input image
     scale: float
-        Free parameter. Higher means larger clusters.
+        Sets the obervation level. Higher means larger clusters.
     sigma: float
         Width of Gaussian kernel used in preprocessing.
     min_size: int
@@ -33,7 +29,7 @@ def _felzenszwalb_segmentation_grey(image, scale=1, sigma=0.8, min_size=20):
 
     Returns
     -------
-    segment_mask: ndarray, [width, height]
+    segment_mask: (height, width) ndarray
         Integer mask indicating segment labels.
     """
     if image.ndim != 2:
diff --git a/skimage/segmentation/quickshift.pyx b/skimage/segmentation/quickshift.pyx
index 37c9294b..a3c5ec9f 100644
--- a/skimage/segmentation/quickshift.pyx
+++ b/skimage/segmentation/quickshift.pyx
@@ -75,15 +75,12 @@ def quickshift(image, ratio=1., kernel_size=5, max_dist=10, return_tree=False,
     cdef np.ndarray[dtype=np.float_t, ndim=3, mode="c"] image_c \
             = np.ascontiguousarray(image) * ratio
 
-    if random_seed is None:
-        random_state = np.random.RandomState()
-    else:
-        random_state = np.random.RandomState(random_seed)
+    random_state = np.random.RandomState(random_seed)
 
     # We compute the distances twice since otherwise
     # we get crazy memory overhead (width * height * windowsize**2)
 
-    # TODO join orphant roots?
+    # TODO join orphaned roots?
     # Some nodes might not have a point of higher density within the
     # search window. We could do a global search over these in the end.
     # Reference implementation doesn't do that, though, and it only has