diff --git a/TODO.txt b/TODO.txt
index f5830f09..1c3d1324 100644
--- a/TODO.txt
+++ b/TODO.txt
@@ -14,4 +14,3 @@ Version 0.10
 ------------
 * Remove backwards-compatability of `skimage.measure.regionprops`
 * Remove deprecated logger function in `skimage/__init__.py`
-* Remove deprecated function `filter.median_filter`
diff --git a/skimage/filter/__init__.py b/skimage/filter/__init__.py
index c8fca735..649eba6b 100644
--- a/skimage/filter/__init__.py
+++ b/skimage/filter/__init__.py
@@ -1,5 +1,4 @@
 from .lpi_filter import inverse, wiener, LPIFilter2D
-from .ctmf import median_filter
 from ._gaussian import gaussian_filter
 from ._canny import canny
 from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt,
@@ -25,7 +24,6 @@ denoise_tv_chambolle = deprecated('skimage.restoration.denoise_tv_chambolle')\
 __all__ = ['inverse',
            'wiener',
            'LPIFilter2D',
-           'median_filter',
            'gaussian_filter',
            'canny',
            'sobel',
diff --git a/skimage/filter/ctmf.py b/skimage/filter/ctmf.py
deleted file mode 100644
index e39aa8bc..00000000
--- a/skimage/filter/ctmf.py
+++ /dev/null
@@ -1,109 +0,0 @@
-"""ctmf.py - constant time per pixel median filtering with an octagonal shape
-
-Reference: S. Perreault and P. Hebert, "Median Filtering in Constant Time",
-IEEE Transactions on Image Processing, September 2007.
-
-Originally part of CellProfiler, code licensed under both GPL and BSD licenses.
-Website: http://www.cellprofiler.org
-Copyright (c) 2003-2009 Massachusetts Institute of Technology
-Copyright (c) 2009-2011 Broad Institute
-All rights reserved.
-Original author: Lee Kamentsky
-"""
-
-import warnings
-import numpy as np
-from . import _ctmf
-from ._rank_order import rank_order
-from .._shared.utils import deprecated
-
-
-@deprecated('filter.rank.median')
-def median_filter(image, radius=2, mask=None, percent=50):
-    """Masked median filter with octagon shape.
-
-    Parameters
-    ----------
-    image : (M, N) ndarray
-        Input image.
-    radius : int
-        Radius (in pixels) of a circle inscribed into the filtering
-        octagon. Must be at least 2. Default radius is 2.
-    mask : (M, N) ndarray
-        Mask with 1's for significant pixels, 0's for masked pixels.
-        By default, all pixels are considered significant.
-    percent : int
-        The unmasked pixels within the octagon are sorted, and the
-        value at `percent` percent of the index range is chosen.
-        Default value of 50 gives the median pixel.
-
-    Returns
-    -------
-    out : (M, N) ndarray
-        Filtered array. In areas where the median filter does
-        not overlap the mask, the filtered result is undefined, but
-        in practice, it will be the lowest value in the valid area.
-
-    Notes
-    -----
-    Because of the histogram implementation, the number of unique values
-    for the output is limited to 256.
-
-    Examples
-    --------
-    >>> a = np.ones((5, 5))
-    >>> a[2, 2] = 10 # introduce outlier
-    >>> b = median_filter(a)
-    >>> b[2, 2] # the median filter is good at removing outliers
-    1.0
-    """
-
-    if image.ndim != 2:
-        raise TypeError("Input 'image' must be a two-dimensional array.")
-
-    if radius < 2:
-        raise ValueError("Input 'radius' must be >= 2.")
-
-    if mask is None:
-        mask = np.ones(image.shape, dtype=np.bool)
-    mask = np.ascontiguousarray(mask, dtype=np.bool)
-
-    if np.all(~ mask):
-        warnings.warn('Mask is all over image! Returning copy of input image.')
-        return image.copy()
-    
-    if (not np.issubdtype(image.dtype, np.int) or
-        np.min(image) < 0 or np.max(image) > 255):
-        ranked_values, translation = rank_order(image[mask])
-        max_ranked_values = np.max(ranked_values)
-        if max_ranked_values == 0:
-            warnings.warn('Particular case? Returning copy of input image.')
-            return image.copy()
-        if max_ranked_values > 255:
-            ranked_values = ranked_values * 255 // max_ranked_values
-        was_ranked = True
-    else:
-        ranked_values = image[mask]
-        was_ranked = False
-    ranked_image = np.zeros(image.shape, np.uint8)
-    ranked_image[mask] = ranked_values
-
-    mask.dtype = np.uint8
-    output = np.zeros(image.shape, np.uint8)
-
-    _ctmf.median_filter(ranked_image, mask, output, radius, percent)
-    if was_ranked:
-        #
-        # The translation gives the original value at each ranking.
-        # We rescale the output to the original ranking and then
-        # use the translation to look up the original value in the image.
-        #
-        if max_ranked_values > 255:
-            result = translation[output.astype(np.uint32) *
-                                 max_ranked_values // 255]
-        else:
-            result = translation[output]
-    else:
-        result = output
-    return result
-
diff --git a/skimage/filter/tests/test_ctmf.py b/skimage/filter/tests/test_ctmf.py
deleted file mode 100644
index c4f6d10e..00000000
--- a/skimage/filter/tests/test_ctmf.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import numpy as np
-from nose.tools import raises
-
-from skimage.filter import median_filter
-
-
-def test_00_00_zeros():
-    '''The median filter on an array of all zeros should be zero'''
-    result = median_filter(np.zeros((10, 10)), 3, np.ones((10, 10), bool))
-    assert np.all(result == 0)
-
-
-def test_00_01_all_masked():
-    '''Test a completely masked image
-
-    Regression test of IMG-1029'''
-    result = median_filter(np.zeros((10, 10)), 3, np.zeros((10, 10), bool))
-    assert (np.all(result == 0))
-
-
-def test_00_02_all_but_one_masked():
-    mask = np.zeros((10, 10), bool)
-    mask[5, 5] = True
-    median_filter(np.zeros((10, 10)), 3, mask)
-
-
-def test_01_01_mask():
-    '''The median filter, masking a single value'''
-    img = np.zeros((10, 10))
-    img[5, 5] = 1
-    mask = np.ones((10, 10), bool)
-    mask[5, 5] = False
-    result = median_filter(img, 3, mask)
-    assert (np.all(result[mask] == 0))
-    np.testing.assert_equal(result[5, 5], 1)
-
-
-def test_02_01_median():
-    '''A median filter larger than the image = median of image'''
-    np.random.seed(0)
-    img = np.random.uniform(size=(9, 9))
-    result = median_filter(img, 20, np.ones((9, 9), bool))
-    np.testing.assert_equal(result[0, 0], np.median(img))
-    assert (np.all(result == np.median(img)))
-
-
-def test_02_02_median_bigger():
-    '''Use an image of more than 255 values to test approximation'''
-    np.random.seed(0)
-    img = np.random.uniform(size=(20, 20))
-    result = median_filter(img, 40, np.ones((20, 20), bool))
-    sorted = np.ravel(img)
-    sorted.sort()
-    min_acceptable = sorted[198]
-    max_acceptable = sorted[202]
-    assert (np.all(result >= min_acceptable))
-    assert (np.all(result <= max_acceptable))
-
-
-def test_03_01_shape():
-    '''Make sure the median filter is the expected octagonal shape'''
-
-    radius = 5
-    a_2 = int(radius / 2.414213)
-    i, j = np.mgrid[-10:11, -10:11]
-    octagon = np.ones((21, 21), bool)
-    #
-    # constrain the octagon mask to be the points that are on
-    # the correct side of the 8 edges
-    #
-    octagon[i < -radius] = False
-    octagon[i > radius] = False
-    octagon[j < -radius] = False
-    octagon[j > radius] = False
-    octagon[i + j < -radius - a_2] = False
-    octagon[j - i > radius + a_2] = False
-    octagon[i + j > radius + a_2] = False
-    octagon[i - j > radius + a_2] = False
-    np.random.seed(0)
-    img = np.random.uniform(size=(21, 21))
-    result = median_filter(img, radius, np.ones((21, 21), bool))
-    sorted = img[octagon]
-    sorted.sort()
-    min_acceptable = sorted[len(sorted) / 2 - 1]
-    max_acceptable = sorted[len(sorted) / 2 + 1]
-    assert (result[10, 10] >= min_acceptable)
-    assert (result[10, 10] <= max_acceptable)
-
-
-def test_04_01_half_masked():
-    '''Make sure that the median filter can handle large masked areas.'''
-    img = np.ones((20, 20))
-    mask = np.ones((20, 20), bool)
-    mask[10:, :] = False
-    img[~ mask] = 2
-    img[1, 1] = 0  # to prevent short circuit for uniform data.
-    result = median_filter(img, 5, mask)
-    # in partial coverage areas, the result should be only
-    # from the masked pixels
-    assert (np.all(result[:14, :] == 1))
-    # in zero coverage areas, the result should be the lowest
-    # value in the valid area
-    assert (np.all(result[15:, :] == np.min(img[mask])))
-
-
-def test_default_values():
-    img = (np.random.random((20, 20)) * 255).astype(np.uint8)
-    mask = np.ones((20, 20), dtype=np.uint8)
-    result1 = median_filter(img, radius=2, mask=mask, percent=50)
-    result2 = median_filter(img)
-    np.testing.assert_array_equal(result1, result2)
-
-
-@raises(ValueError)
-def test_insufficient_size():
-    img = (np.random.random((20, 20)) * 255).astype(np.uint8)
-    median_filter(img, radius=1)
-
-
-@raises(TypeError)
-def test_wrong_shape():
-    img = np.empty((10, 10, 3))
-    median_filter(img)
-
-
-if __name__ == "__main__":
-    np.testing.run_module_suite()