diff --git a/skimage/filter/_thresholding.pyx b/skimage/filter/_thresholding.pyx
index 303c1a5f..88badf31 100644
--- a/skimage/filter/_thresholding.pyx
+++ b/skimage/filter/_thresholding.pyx
@@ -19,6 +19,9 @@ def _adaptive_threshold(
         sigma = (block_size - 1) / 6.0
         mean_image = scipy.ndimage.gaussian_filter(image, sigma)
     elif method == 'mean':
+        mask = 1. / block_size**2 * np.ones((block_size, block_size))
+        mean_image = scipy.ndimage.convolve(image, mask)
+    elif method == 'median':
         mean_image = scipy.ndimage.median_filter(image, block_size)
 
     for r in range(image.shape[0]):
diff --git a/skimage/filter/thresholding.py b/skimage/filter/thresholding.py
index d0d95a4a..09e6eaea 100644
--- a/skimage/filter/thresholding.py
+++ b/skimage/filter/thresholding.py
@@ -25,8 +25,8 @@ def adaptive_threshold(image, block_size, offset, method='gaussian'):
         constant subtracted from weighted mean of neighborhood to calculate
         the local threshold value
     method : string, optional
-        thresholding type which must be one of `gaussian` or `mean`.
-        By default the `gaussian` method is used.
+        thresholding type which must be one of 'gaussian', 'mean' or 'median'.
+        By default the 'gaussian' method is used.
 
     Returns
     -------