diff --git a/doc/examples/plot_segmentations.py b/doc/examples/plot_segmentations.py
index 09012bbd..a8ee7cea 100644
--- a/doc/examples/plot_segmentations.py
+++ b/doc/examples/plot_segmentations.py
@@ -12,7 +12,7 @@ a basis for more sophisticated algorithms such as CRFs.
 
 Felzenszwalb's efficient graph based segmentation
 -------------------------------------------------
-This fast 2d image segmentation algorithm, proposed in [1]_ is popular in the
+This fast 2D image segmentation algorithm, proposed in [1]_ is popular in the
 computer vision community.
 The algorithm has a single ``scale`` parameter that influences the segment
 size. The actual size and number of segments can vary greatly, depending on
@@ -25,9 +25,9 @@ local contrast.
 Quickshift image segmentation
 -----------------------------
 
-Quickshift is a relatively recent 2d image segmentation algorithm, based on an
+Quickshift is a relatively recent 2D image segmentation algorithm, based on an
 approximation of kernelized mean-shift. Therefore it belongs to the family of
-local mode-seeking algorithms and is applied to the 5d space consisting of
+local mode-seeking algorithms and is applied to the 5D space consisting of
 color information and image location [2]_.
 
 One of the benefits of quickshift is that it actually computes a
diff --git a/skimage/segmentation/_felzenszwalb.py b/skimage/segmentation/_felzenszwalb.py
index 5729bd95..67971a96 100644
--- a/skimage/segmentation/_felzenszwalb.py
+++ b/skimage/segmentation/_felzenszwalb.py
@@ -1,7 +1,7 @@
 import warnings
 import numpy as np
 
-from .felzenszwalb_cy import _felzenszwalb_grey
+from ._felzenszwalb_cy import _felzenszwalb_grey
 
 
 def felzenszwalb(image, scale=1, sigma=0.8, min_size=20):
@@ -60,7 +60,7 @@ def felzenszwalb(image, scale=1, sigma=0.8, min_size=20):
                       " wanted?" % image.shape[2])
     segmentations = []
     # compute quickshift for each channel
-    for c in xrange(n_channels):
+    for c in range(n_channels):
         channel = np.ascontiguousarray(image[:, :, c])
         s = _felzenszwalb_grey(channel, scale=scale, sigma=sigma,
                                min_size=min_size)
diff --git a/skimage/segmentation/felzenszwalb_cy.pyx b/skimage/segmentation/_felzenszwalb_cy.pyx
similarity index 100%
rename from skimage/segmentation/felzenszwalb_cy.pyx
rename to skimage/segmentation/_felzenszwalb_cy.pyx
diff --git a/skimage/segmentation/_slic.pyx b/skimage/segmentation/_slic.pyx
index a4f37fb2..ecb58efe 100644
--- a/skimage/segmentation/_slic.pyx
+++ b/skimage/segmentation/_slic.pyx
@@ -45,7 +45,7 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1,
     """
     image = np.atleast_3d(image)
     if image.shape[2] != 3:
-        ValueError("Only 3-channel 2d images are supported.")
+        ValueError("Only 3-channel 2D images are supported.")
     image = ndimage.gaussian_filter(img_as_float(image), [sigma, sigma, 0])
     if convert2lab:
         image = rgb2lab(image)
@@ -82,21 +82,21 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1,
     cdef np.float_t* current_distance
     cdef np.float_t* current_pixel
     cdef double tmp
-    for i in xrange(max_iter):
+    for i in range(max_iter):
         distance.fill(np.inf)
         changes = 0
         current_mean = <np.float_t*> means.data
         # assign pixels to means
-        for k in xrange(n_means):
+        for k in range(n_means):
             # compute windows:
             y_min = int(max(current_mean[0] - 2 * step, 0))
             y_max = int(min(current_mean[0] + 2 * step, height))
             x_min = int(max(current_mean[1] - 2 * step, 0))
             x_max = int(min(current_mean[1] + 2 * step, width))
-            for y in xrange(y_min, y_max):
+            for y in range(y_min, y_max):
                 current_pixel = &image_p[5 * (y * width + x_min)]
                 current_distance = &distance_p[y * width + x_min]
-                for x in xrange(x_min, x_max):
+                for x in range(x_min, x_max):
                     mean_entry = current_mean
                     dist_mean = 0
                     for c in range(5):
@@ -117,7 +117,7 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1,
             break
         # recompute means:
         means_list = [np.bincount(nearest_mean.ravel(),
-                      image_yx[:, :, j].ravel()) for j in xrange(5)]
+                      image_yx[:, :, j].ravel()) for j in range(5)]
         in_mean = np.bincount(nearest_mean.ravel())
         in_mean[in_mean == 0] = 1
         means = (np.vstack(means_list) / in_mean).T.copy("C")
diff --git a/skimage/segmentation/setup.py b/skimage/segmentation/setup.py
index ec092ffe..b9e19078 100644
--- a/skimage/segmentation/setup.py
+++ b/skimage/segmentation/setup.py
@@ -11,8 +11,8 @@ def configuration(parent_package='', top_path=None):
 
     config = Configuration('segmentation', parent_package, top_path)
 
-    cython(['felzenszwalb_cy.pyx'], working_path=base_path)
-    config.add_extension('felzenszwalb_cy', sources=['felzenszwalb_cy.c'],
+    cython(['_felzenszwalb_cy.pyx'], working_path=base_path)
+    config.add_extension('_felzenszwalb_cy', sources=['_felzenszwalb_cy.c'],
                          include_dirs=[get_numpy_include_dirs()])
     cython(['_quickshift.pyx'], working_path=base_path)
     config.add_extension('_quickshift', sources=['_quickshift.c'],