diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py
index a05ea476b9de..0d5c73e7e467 100644
--- a/numpy/ma/extras.py
+++ b/numpy/ma/extras.py
@@ -708,34 +708,37 @@ def _median(a, axis=None, out=None, overwrite_input=False):
             asorted = a
     else:
         asorted = sort(a, axis=axis)
+
     if axis is None:
         axis = 0
     elif axis < 0:
-        axis += a.ndim
+        axis += asorted.ndim
 
     if asorted.ndim == 1:
         idx, odd = divmod(count(asorted), 2)
-        return asorted[idx - (not odd) : idx + 1].mean()
+        return asorted[idx + odd - 1 : idx + 1].mean(out=out)
 
-    counts = asorted.shape[axis] - (asorted.mask).sum(axis=axis)
+    counts = count(asorted, axis=axis)
     h = counts // 2
+
     # create indexing mesh grid for all but reduced axis
     axes_grid = [np.arange(x) for i, x in enumerate(asorted.shape)
                  if i != axis]
     ind = np.meshgrid(*axes_grid, sparse=True, indexing='ij')
+
     # insert indices of low and high median
     ind.insert(axis, h - 1)
     low = asorted[tuple(ind)]
     low._sharedmask = False
     ind[axis] = h
     high = asorted[tuple(ind)]
+
     # duplicate high if odd number of elements so mean does nothing
     odd = counts % 2 == 1
-    if asorted.ndim == 1:
-        if odd:
-            low = high
-    else:
-        low[odd] = high[odd]
+    if asorted.ndim > 1:
+        np.copyto(low, high, where=odd)
+    elif odd:
+        low = high
 
     if np.issubdtype(asorted.dtype, np.inexact):
         # avoid inf / x = masked
diff --git a/numpy/ma/tests/test_extras.py b/numpy/ma/tests/test_extras.py
index 6d56d4dc6c92..27fac3d635a8 100644
--- a/numpy/ma/tests/test_extras.py
+++ b/numpy/ma/tests/test_extras.py
@@ -10,6 +10,7 @@
 from __future__ import division, absolute_import, print_function
 
 import warnings
+import itertools
 
 import numpy as np
 from numpy.testing import (
@@ -684,6 +685,37 @@ def test_docstring_examples(self):
         assert_equal(ma_x.shape, (2,), "shape mismatch")
         assert_(type(ma_x) is MaskedArray)
 
+    def test_axis_argument_errors(self):
+        msg = "mask = %s, ndim = %s, axis = %s, overwrite_input = %s"
+        for ndmin in range(5):
+            for mask in [False, True]:
+                x = array(1, ndmin=ndmin, mask=mask)
+
+                # Valid axis values should not raise exception
+                args = itertools.product(range(-ndmin, ndmin), [False, True])
+                for axis, over in args:
+                    try:
+                        np.ma.median(x, axis=axis, overwrite_input=over)
+                    except:
+                        raise AssertionError(msg % (mask, ndmin, axis, over))
+
+                # Invalid axis values should raise exception
+                args = itertools.product([-(ndmin + 1), ndmin], [False, True])
+                for axis, over in args:
+                    try:
+                        np.ma.median(x, axis=axis, overwrite_input=over)
+                    except IndexError:
+                        pass
+                    else:
+                        raise AssertionError(msg % (mask, ndmin, axis, over))
+
+    def test_masked_0d(self):
+        # Check values
+        x = array(1, mask=False)
+        assert_equal(np.ma.median(x), 1)
+        x = array(1, mask=True)
+        assert_equal(np.ma.median(x), np.ma.masked)
+
     def test_masked_1d(self):
         x = array(np.arange(5), mask=True)
         assert_equal(np.ma.median(x), np.ma.masked)