diff --git a/skimage/segmentation/active_contour_model.py b/skimage/segmentation/active_contour_model.py
index b4d61904..d5aeb0c3 100644
--- a/skimage/segmentation/active_contour_model.py
+++ b/skimage/segmentation/active_contour_model.py
@@ -79,7 +79,7 @@ def active_contour(image, snake, alpha=0.01, beta=0.1,
 
     Fit spline to image:
 
-    >>> snake = active_contour_model(img, init, w_edge=0, w_line=1)
+    >>> snake = active_contour(img, init, w_edge=0, w_line=1)
     >>> int(np.mean(np.sqrt((45-snake[:, 0])**2 + (35-snake[:, 1])**2)))
     25
 
@@ -88,10 +88,9 @@ def active_contour(image, snake, alpha=0.01, beta=0.1,
     new_scipy = scipy_version[0] > 0 or \
                 (scipy_version[0] == 0 and scipy_version[1] >= 14)
     if not new_scipy:
-        warnings.warn('You are using an old version of scipy. '
-                      'Upgrading to a version newer than 0.14.0 '
-                      'will signifcantly improve the performance '
-                      'of this algorithm.')
+        raise NotImplementedError('You are using an old version of scipy. '
+                      'Active contours is implemented for scipy versions '
+                      '0.14.0 and above.')
 
     max_iterations = int(max_iterations)
     if max_iterations <= 0:
@@ -112,7 +111,7 @@ def active_contour(image, snake, alpha=0.01, beta=0.1,
                     sobel(img[:, :, 2])]
         else:
             edge = [sobel(img)]
-        for i in xrange(3 if RGB else 1):
+        for i in range(3 if RGB else 1):
             edge[i][0, :] = edge[i][1, :]
             edge[i][-1, :] = edge[i][-2, :]
             edge[i][:, 0] = edge[i][:, 1]
@@ -184,7 +183,7 @@ def active_contour(image, snake, alpha=0.01, beta=0.1,
     inv = scipy.linalg.inv(A+gamma*np.eye(n))
 
     # Explicit time stepping for image energy minimization:
-    for i in xrange(max_iterations):
+    for i in range(max_iterations):
         if new_scipy:
             fx = intp(x, y, dx=1, grid=False)
             fy = intp(x, y, dy=1, grid=False)
diff --git a/skimage/segmentation/tests/test_active_contour_model.py b/skimage/segmentation/tests/test_active_contour_model.py
index b95f87d7..3b529f67 100644
--- a/skimage/segmentation/tests/test_active_contour_model.py
+++ b/skimage/segmentation/tests/test_active_contour_model.py
@@ -4,8 +4,13 @@ from skimage.color import rgb2gray
 from skimage.filters import gaussian_filter
 from skimage.segmentation import active_contour
 from numpy.testing import assert_equal, assert_allclose, assert_raises
+from numpy.testing.decorators import skipif
 
+scipy_version = list(map(int, scipy.__version__.split('.')))
+new_scipy = scipy_version[0] > 0 or \
+            (scipy_version[0] == 0 and scipy_version[1] >= 14)
 
+@skipif(not new_scipy)
 def test_periodic_reference():
     img = data.astronaut()
     img = rgb2gray(img)
@@ -20,7 +25,7 @@ def test_periodic_reference():
     assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx)
     assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy)
 
-
+@skipif(not new_scipy)
 def test_fixed_reference():
     img = data.text()
     x = np.linspace(5, 424, 100)
@@ -33,7 +38,7 @@ def test_fixed_reference():
     assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx)
     assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy)
 
-
+@skipif(not new_scipy)
 def test_free_reference():
     img = data.text()
     x = np.linspace(5, 424, 100)
@@ -46,7 +51,7 @@ def test_free_reference():
     assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx)
     assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy)
 
-
+@skipif(not new_scipy)
 def test_RGB():
     img = gaussian_filter(data.text(), 1)
     imgR = np.zeros((img.shape[0], img.shape[1], 3))
@@ -73,7 +78,7 @@ def test_RGB():
     assert_equal(np.array(snake[:10, 0], dtype=np.int32), refx)
     assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy)
 
-
+@skipif(not new_scipy)
 def test_end_points():
     img = data.astronaut()
     img = rgb2gray(img)
@@ -94,7 +99,7 @@ def test_end_points():
             gamma=0.001, max_iterations=100)
     assert_allclose(snake[0, :], [x[0], y[0]], atol=1e-5)
 
-
+@skipif(not new_scipy)
 def test_bad_input():
     img = np.zeros((10, 10))
     x = np.linspace(5, 424, 100)