diff --git a/skimage/segmentation/_clear_border.py b/skimage/segmentation/_clear_border.py
index 266e17e2..318f8790 100644
--- a/skimage/segmentation/_clear_border.py
+++ b/skimage/segmentation/_clear_border.py
@@ -1,37 +1,39 @@
 import numpy as np
-from scipy.ndimage import label
+#from scipy.ndimage import label
+from ..measure import label
 
 
-def clear_border(image, buffer_size=0, bgval=0):
-    """Clear objects connected to image border.
+def clear_border(labels, buffer_size=0, bgval=0):
+    """Clear objects connected to the label image border.
 
-    The changes will be applied to the input image.
+    The changes will be applied directly to the input.
 
     Parameters
     ----------
-    image : (N, M) array
-        Binary image.
+    labels : (N, M) array of int
+        Label or binary image.
     buffer_size : int, optional
-        Define additional buffer around image border.
+        The width of the border examined.  By default, only objects
+        that touch the outside of the image are removed.
     bgval : float or int, optional
-        Value for cleared objects.
+        Cleared objects are set to this value.
 
     Returns
     -------
-    image : (N, M) array
-        Cleared binary image.
+    labels : (N, M) array
+        Cleared binary image.  Note that the input label image is modified.
 
     Examples
     --------
     >>> import numpy as np
     >>> from skimage.segmentation import clear_border
-    >>> image = np.array([[0, 0, 0, 0, 0, 0, 0, 1, 0],
-    ...                   [0, 0, 0, 0, 1, 0, 0, 0, 0],
-    ...                   [1, 0, 0, 1, 0, 1, 0, 0, 0],
-    ...                   [0, 0, 1, 1, 1, 1, 1, 0, 0],
-    ...                   [0, 1, 1, 1, 1, 1, 1, 1, 0],
-    ...                   [0, 0, 0, 0, 0, 0, 0, 0, 0]])
-    >>> clear_border(image)
+    >>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 1, 0],
+    ...                    [0, 0, 0, 0, 1, 0, 0, 0, 0],
+    ...                    [1, 0, 0, 1, 0, 1, 0, 0, 0],
+    ...                    [0, 0, 1, 1, 1, 1, 1, 0, 0],
+    ...                    [0, 1, 1, 1, 1, 1, 1, 1, 0],
+    ...                    [0, 0, 0, 0, 0, 0, 0, 0, 0]])
+    >>> clear_border(labels)
     array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
            [0, 0, 0, 0, 1, 0, 0, 0, 0],
            [0, 0, 0, 1, 0, 1, 0, 0, 0],
@@ -40,6 +42,7 @@ def clear_border(image, buffer_size=0, bgval=0):
            [0, 0, 0, 0, 0, 0, 0, 0, 0]])
 
     """
+    image = labels
 
     rows, cols = image.shape
     if buffer_size >= rows or buffer_size >= cols:
@@ -53,7 +56,10 @@ def clear_border(image, buffer_size=0, bgval=0):
     borders[:, :ext] = True
     borders[:, - ext:] = True
 
-    labels, number = label(image)
+    # Re-label, in case we are dealing with a binary image
+    # and to get consistent labeling
+    labels = label(image, background=0) + 1
+    number = np.max(labels) + 1
 
     # determine all objects that are connected to borders
     borders_indices = np.unique(labels[borders])
diff --git a/skimage/segmentation/tests/test_clear_border.py b/skimage/segmentation/tests/test_clear_border.py
index 5d6852cf..67739426 100644
--- a/skimage/segmentation/tests/test_clear_border.py
+++ b/skimage/segmentation/tests/test_clear_border.py
@@ -24,9 +24,24 @@ def test_clear_border():
     assert_array_equal(result, np.zeros(result.shape))
 
     # test background value
-    result = clear_border(image.copy(), 1, 2)
+    result = clear_border(image.copy(), buffer_size=1, bgval=2)
     assert_array_equal(result, 2 * np.ones_like(image))
 
 
+def test_clear_border_non_binary():
+    image = np.array([[1, 2, 3, 1, 2],
+                      [3, 4, 5, 4, 2],
+                      [3, 4, 5, 4, 2],
+                      [3, 3, 2, 1, 2]])
+
+    result = clear_border(image.copy())
+    expected = np.array([[0, 0, 0, 0, 0],
+                         [0, 4, 5, 4, 0],
+                         [0, 4, 5, 4, 0],
+                         [0, 0, 0, 0, 0]])
+
+    assert_array_equal(result, expected)
+
+
 if __name__ == "__main__":
     np.testing.run_module_suite()