diff --git a/TODO.txt b/TODO.txt
index e2fc4218..f17b088f 100644
--- a/TODO.txt
+++ b/TODO.txt
@@ -3,6 +3,7 @@ Remember to list any API changes below in `doc/source/api_changes.txt`.
 Version 0.13
 ------------
 * Remove deprecated `None` defaults for `skimage.exposure.rescale_intensity`
+* Remove deprecated `skimage.filter.canny` import in __init__.py that is now in `skimage.feature.canny`
 
 Version 0.12
 ------------
diff --git a/doc/examples/applications/plot_coins_segmentation.py b/doc/examples/applications/plot_coins_segmentation.py
index 49ff399f..50eec0bb 100644
--- a/doc/examples/applications/plot_coins_segmentation.py
+++ b/doc/examples/applications/plot_coins_segmentation.py
@@ -57,7 +57,7 @@ segmentation. To do this, we first get the edges of features using the Canny
 edge-detector.
 """
 
-from skimage.filter import canny
+from skimage.feature import canny
 edges = canny(coins/255.)
 
 fig, ax = plt.subplots(figsize=(4, 3))
diff --git a/doc/examples/plot_canny.py b/doc/examples/plot_canny.py
index f1caf264..82a930e5 100644
--- a/doc/examples/plot_canny.py
+++ b/doc/examples/plot_canny.py
@@ -19,7 +19,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 from scipy import ndimage
 
-from skimage import filter
+from skimage import feature
 
 
 # Generate noisy image of a square
@@ -31,8 +31,8 @@ im = ndimage.gaussian_filter(im, 4)
 im += 0.2 * np.random.random(im.shape)
 
 # Compute the Canny filter for two values of sigma
-edges1 = filter.canny(im)
-edges2 = filter.canny(im, sigma=3)
+edges1 = feature.canny(im)
+edges2 = feature.canny(im, sigma=3)
 
 # display results
 fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3))
diff --git a/doc/examples/plot_circular_elliptical_hough_transform.py b/doc/examples/plot_circular_elliptical_hough_transform.py
index fbdd4f2c..9b0be0fb 100755
--- a/doc/examples/plot_circular_elliptical_hough_transform.py
+++ b/doc/examples/plot_circular_elliptical_hough_transform.py
@@ -37,16 +37,16 @@ Its size is extended by two times the larger radius.
 import numpy as np
 import matplotlib.pyplot as plt
 
-from skimage import data, filter, color
+from skimage import data, color
 from skimage.transform import hough_circle
-from skimage.feature import peak_local_max
+from skimage.feature import peak_local_max, canny
 from skimage.draw import circle_perimeter
 from skimage.util import img_as_ubyte
 
 
 # Load picture and detect edges
 image = img_as_ubyte(data.coins()[0:95, 70:370])
-edges = filter.canny(image, sigma=3, low_threshold=10, high_threshold=50)
+edges = canny(image, sigma=3, low_threshold=10, high_threshold=50)
 
 fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(5, 2))
 
@@ -106,14 +106,15 @@ References
 
 import matplotlib.pyplot as plt
 
-from skimage import data, filter, color
+from skimage import data, color
+from skimage.feature import canny
 from skimage.transform import hough_ellipse
 from skimage.draw import ellipse_perimeter
 
 # Load picture, convert to grayscale and detect edges
 image_rgb = data.coffee()[0:220, 160:420]
 image_gray = color.rgb2gray(image_rgb)
-edges = filter.canny(image_gray, sigma=2.0,
+edges = canny(image_gray, sigma=2.0,
                      low_threshold=0.55, high_threshold=0.8)
 
 # Perform a Hough Transform
diff --git a/doc/examples/plot_line_hough_transform.py b/doc/examples/plot_line_hough_transform.py
index bdb05661..4293c409 100644
--- a/doc/examples/plot_line_hough_transform.py
+++ b/doc/examples/plot_line_hough_transform.py
@@ -58,7 +58,7 @@ References
 
 from skimage.transform import (hough_line, hough_line_peaks,
                                probabilistic_hough_line)
-from skimage.filter import canny
+from skimage.feature import canny
 from skimage import data
 
 import numpy as np
diff --git a/doc/source/user_guide/tutorial_segmentation.txt b/doc/source/user_guide/tutorial_segmentation.txt
index cff7c651..d183102f 100644
--- a/doc/source/user_guide/tutorial_segmentation.txt
+++ b/doc/source/user_guide/tutorial_segmentation.txt
@@ -38,11 +38,11 @@ Edge-based segmentation
 
 Let us first try to detect edges that enclose the coins. For edge
 detection, we use the `Canny detector 
-<http://en.wikipedia.org/wiki/Canny_edge_detector>`_ of ``skimage.filter.canny``
+<http://en.wikipedia.org/wiki/Canny_edge_detector>`_ of ``skimage.feature.canny``
 
 ::
 
-    >>> from skimage.filter import canny
+    >>> from skimage.feature import canny
     >>> edges = canny(coins/255.)
 
 As the background is very smooth, almost all edges are found at the