diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py
index 200aa5dc..7e853f06 100644
--- a/skimage/transform/hough_transform.py
+++ b/skimage/transform/hough_transform.py
@@ -132,8 +132,9 @@ def hough_circle(image, radius, normalize=True, full_output=False):
     ----------
     image : (M, N) ndarray
         Input image with nonzero values representing edges.
-    radius : ndarray
+    radius : scalar or sequence of scalars
         Radii at which to compute the Hough transform.
+        Floats are converted to integers.
     normalize : boolean, optional (default True)
         Normalize the accumulator with the number
         of pixels used to draw the radius.
@@ -163,5 +164,7 @@ def hough_circle(image, radius, normalize=True, full_output=False):
     (25, 35, 23)
 
     """
+
+    radius = np.atleast_1d(np.asarray(radius))
     return _hough_circle(image, radius.astype(np.intp),
                          normalize=normalize, full_output=full_output)
diff --git a/skimage/transform/tests/test_hough_transform.py b/skimage/transform/tests/test_hough_transform.py
index e7030494..fce7013c 100644
--- a/skimage/transform/tests/test_hough_transform.py
+++ b/skimage/transform/tests/test_hough_transform.py
@@ -140,8 +140,11 @@ def test_hough_circle():
     y, x = circle_perimeter(y_0, x_0, radius)
     img[x, y] = 1
 
+    out1 = tf.hough_circle(img, radius)
+    out2 = tf.hough_circle(img, [radius])
+    assert_equal(out1, out2)
     out = tf.hough_circle(img, np.array([radius], dtype=np.intp))
-
+    assert_equal(out, out1)
     x, y = np.where(out[0] == out[0].max())
     assert_equal(x[0], x_0)
     assert_equal(y[0], y_0)