diff --git a/doc/__init__.py b/doc/__init__.py
new file mode 100644
index 00000000..10b6fb15
--- /dev/null
+++ b/doc/__init__.py
@@ -0,0 +1 @@
+__author__ = 'olivier'
diff --git a/doc/examples/plot_benchmark_rank.py b/doc/examples/applications/plot_benchmark_rank.py
similarity index 96%
rename from doc/examples/plot_benchmark_rank.py
rename to doc/examples/applications/plot_benchmark_rank.py
index dab2f8d4..79357768 100644
--- a/doc/examples/plot_benchmark_rank.py
+++ b/doc/examples/applications/plot_benchmark_rank.py
@@ -19,13 +19,14 @@ import numpy as np
 import matplotlib.pyplot as plt
 import time
 
+from scipy.ndimage.filters import percentile_filter
+
 from skimage import data
 from skimage.morphology import dilation,disk
 from skimage.filter import median_filter
-from scipy.ndimage.filters import percentile_filter
-import skimage.rank as rank
+import skimage.filter.rank as rank
 
-def log_timing(func):
+def exec_and_timeit(func):
     """ Decorator that returns both function results and execution time
     (result, ms)
     """
@@ -38,23 +39,23 @@ def log_timing(func):
     return wrapper
 
 
-@log_timing
+@exec_and_timeit
 def cr_med(image,selem):
     return rank.median(image=image,selem = selem)
 
-@log_timing
+@exec_and_timeit
 def cr_max(image,selem):
     return rank.maximum(image=image,selem = selem)
 
-@log_timing
+@exec_and_timeit
 def cm_dil(image,selem):
     return dilation(image=image,selem = selem)
 
-@log_timing
+@exec_and_timeit
 def ctmf_med(image,radius):
     return median_filter(image=image,radius=radius)
 
-@log_timing
+@exec_and_timeit
 def ndi_med(image,n):
     return percentile_filter(image,50,size=n*2-1)
 
@@ -84,8 +85,6 @@ def compare_dilate():
     plt.title('increasing element size')
     plt.plot(e_range,rec)
     plt.legend(['crank.maximum','cmorph.dilate'])
-    plt.figure()
-    plt.imshow(np.hstack((rc,rcm)))
 
     r = 9
     elem = disk(r+1)
diff --git a/doc/examples/plot_16bitbilateral.py b/doc/examples/plot_16bitbilateral.py
index f61f3c55..076d03c4 100644
--- a/doc/examples/plot_16bitbilateral.py
+++ b/doc/examples/plot_16bitbilateral.py
@@ -11,7 +11,7 @@ import matplotlib.pyplot as plt
 
 from skimage import data
 from skimage.morphology import disk
-import skimage.rank as rank
+import skimage.filter.rank as rank
 
 a8 = (data.coins()).astype('uint8')
 
@@ -23,11 +23,13 @@ selem = disk(50)
 f3 = rank.equalize(a16,selem = selem)
 
 # display results
-fig, axes = plt.subplots(nrows=3, figsize=(15,5))
+fig, axes = plt.subplots(nrows=3, figsize=(15,15))
 ax0, ax1, ax2 = axes
 
 ax0.imshow(np.hstack((a8,f1)))
+ax0.set_title('percentile mean')
 ax1.imshow(np.hstack((a16,f2)))
+ax1.set_title('bilateral mean')
 ax2.imshow(np.hstack((a16,f3)))
-
+ax2.set_title('local equalization')
 plt.show()
diff --git a/doc/examples/plot_lena_bilateral_denoise.py b/doc/examples/plot_lena_bilateral_denoise.py
index 57593867..969403d0 100644
--- a/doc/examples/plot_lena_bilateral_denoise.py
+++ b/doc/examples/plot_lena_bilateral_denoise.py
@@ -18,7 +18,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 from skimage import data, color, img_as_ubyte
-from skimage.rank import bilateral_mean
+from skimage.filter.rank import bilateral_mean
 from skimage.morphology import disk
 
 l = img_as_ubyte(color.rgb2gray(data.lena()))
diff --git a/doc/examples/plot_local_autolevels.py b/doc/examples/plot_local_autolevels.py
index 842215d5..7c750141 100644
--- a/doc/examples/plot_local_autolevels.py
+++ b/doc/examples/plot_local_autolevels.py
@@ -13,7 +13,7 @@ import numpy as np
 
 from skimage import data
 
-from skimage.rank import percentile_autolevel,autolevel
+from skimage.filter.rank import percentile_autolevel,autolevel
 from skimage.morphology import disk
 
 
diff --git a/doc/examples/plot_local_equalize.py b/doc/examples/plot_local_equalize.py
index 897989f0..1a431f5c 100644
--- a/doc/examples/plot_local_equalize.py
+++ b/doc/examples/plot_local_equalize.py
@@ -17,12 +17,12 @@ The local version [2]_ of the histogram equalization emphasized every local gray
 from skimage import data
 from skimage.util.dtype import dtype_range
 from skimage import exposure
-from skimage import rank
 from skimage.morphology import disk
 
 import matplotlib.pyplot as plt
 
 import numpy as np
+from skimage.filter import rank
 
 def plot_img_and_hist(img, axes, bins=256):
     """Plot an image along with its histogram and cumulative histogram.
diff --git a/doc/examples/plot_local_threshold.py b/doc/examples/plot_local_threshold.py
index c5810156..8bc79b30 100644
--- a/doc/examples/plot_local_threshold.py
+++ b/doc/examples/plot_local_threshold.py
@@ -27,7 +27,7 @@ import matplotlib.pyplot as plt
 from skimage import data
 from skimage.filter import threshold_otsu, threshold_adaptive
 
-from skimage.rank import threshold,morph_contr_enh
+from skimage.filter.rank import threshold,morph_contr_enh
 from skimage.morphology import disk
 
 
diff --git a/doc/examples/plot_marked_watershed.py b/doc/examples/plot_marked_watershed.py
index 0be25007..738a3d24 100644
--- a/doc/examples/plot_marked_watershed.py
+++ b/doc/examples/plot_marked_watershed.py
@@ -14,15 +14,14 @@ See Wikipedia_ for more details on the algorithm.
 
 """
 
-import numpy as np
 from scipy import ndimage
 import matplotlib.pyplot as plt
 from skimage.morphology import watershed,disk
-from skimage import rank
 from skimage import data
-from scipy import ndimage
 
 # original data
+from skimage.filter import rank
+
 image = data.camera()
 
 # denoise image
diff --git a/skimage/rank/README.rst b/skimage/filter/rank/README.rst
similarity index 100%
rename from skimage/rank/README.rst
rename to skimage/filter/rank/README.rst
diff --git a/skimage/rank/__init__.py b/skimage/filter/rank/__init__.py
similarity index 100%
rename from skimage/rank/__init__.py
rename to skimage/filter/rank/__init__.py
diff --git a/skimage/rank/_core16.pxd b/skimage/filter/rank/_core16.pxd
similarity index 100%
rename from skimage/rank/_core16.pxd
rename to skimage/filter/rank/_core16.pxd
diff --git a/skimage/rank/_core16.pyx b/skimage/filter/rank/_core16.pyx
similarity index 100%
rename from skimage/rank/_core16.pyx
rename to skimage/filter/rank/_core16.pyx
diff --git a/skimage/rank/_core8.pxd b/skimage/filter/rank/_core8.pxd
similarity index 100%
rename from skimage/rank/_core8.pxd
rename to skimage/filter/rank/_core8.pxd
diff --git a/skimage/rank/_core8.pyx b/skimage/filter/rank/_core8.pyx
similarity index 100%
rename from skimage/rank/_core8.pyx
rename to skimage/filter/rank/_core8.pyx
diff --git a/skimage/rank/_crank16.pyx b/skimage/filter/rank/_crank16.pyx
similarity index 99%
rename from skimage/rank/_crank16.pyx
rename to skimage/filter/rank/_crank16.pyx
index ce8510db..57d41563 100644
--- a/skimage/rank/_crank16.pyx
+++ b/skimage/filter/rank/_crank16.pyx
@@ -15,7 +15,7 @@ import numpy as np
 cimport numpy as np
 
 # import main loop
-from _core16 cimport _core16
+from skimage.filter.rank._core16 cimport _core16
 
 # -----------------------------------------------------------------
 # kernels uint16 take extra parameter for defining the bitdepth
diff --git a/skimage/rank/_crank16_bilateral.pyx b/skimage/filter/rank/_crank16_bilateral.pyx
similarity index 98%
rename from skimage/rank/_crank16_bilateral.pyx
rename to skimage/filter/rank/_crank16_bilateral.pyx
index b5103be4..24016bbf 100644
--- a/skimage/rank/_crank16_bilateral.pyx
+++ b/skimage/filter/rank/_crank16_bilateral.pyx
@@ -15,7 +15,7 @@ import numpy as np
 cimport numpy as np
 
 # import main loop
-from _core16 cimport _core16
+from skimage.filter.rank._core16 cimport _core16
 
 # -----------------------------------------------------------------
 # kernels uint16 take extra parameter for defining the bitdepth
diff --git a/skimage/rank/_crank16_percentiles.pyx b/skimage/filter/rank/_crank16_percentiles.pyx
similarity index 99%
rename from skimage/rank/_crank16_percentiles.pyx
rename to skimage/filter/rank/_crank16_percentiles.pyx
index 527d2aed..73ccde68 100644
--- a/skimage/rank/_crank16_percentiles.pyx
+++ b/skimage/filter/rank/_crank16_percentiles.pyx
@@ -7,7 +7,7 @@ import numpy as np
 cimport numpy as np
 
 # import main loop
-from _core16 cimport _core16, int_min, int_max
+from skimage.filter.rank._core16 cimport _core16, int_min, int_max
 
 # -----------------------------------------------------------------
 # kernels uint16 (SOFT version using percentiles)
diff --git a/skimage/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx
similarity index 99%
rename from skimage/rank/_crank8.pyx
rename to skimage/filter/rank/_crank8.pyx
index 94124cf7..045e3645 100644
--- a/skimage/rank/_crank8.pyx
+++ b/skimage/filter/rank/_crank8.pyx
@@ -15,7 +15,7 @@ import numpy as np
 cimport numpy as np
 
 # import main loop
-from _core8 cimport _core8
+from skimage.filter.rank._core8 cimport _core8
 
 # -----------------------------------------------------------------
 # kernels uint8
diff --git a/skimage/rank/_crank8_percentiles.pyx b/skimage/filter/rank/_crank8_percentiles.pyx
similarity index 99%
rename from skimage/rank/_crank8_percentiles.pyx
rename to skimage/filter/rank/_crank8_percentiles.pyx
index 5441eac7..f882961a 100644
--- a/skimage/rank/_crank8_percentiles.pyx
+++ b/skimage/filter/rank/_crank8_percentiles.pyx
@@ -7,7 +7,7 @@ import numpy as np
 cimport numpy as np
 
 # import main loop
-from _core8 cimport _core8, uint8_max, uint8_min
+from skimage.filter.rank._core8 cimport _core8, uint8_max, uint8_min
 
 # -----------------------------------------------------------------
 # kernels uint8 (SOFT version using percentiles)
diff --git a/skimage/rank/bilateral_rank.py b/skimage/filter/rank/bilateral_rank.py
similarity index 85%
rename from skimage/rank/bilateral_rank.py
rename to skimage/filter/rank/bilateral_rank.py
index 24ccbcd0..1dc7552d 100644
--- a/skimage/rank/bilateral_rank.py
+++ b/skimage/filter/rank/bilateral_rank.py
@@ -1,17 +1,32 @@
-"""
+"""bilateral_rank.py - approximate bilateral rankfilter for local (custom kernel) mean
 
-note: 8 bit images are casted into 16 bit image here
+The local histogram is computed using a sliding window similar to the method described in
+
+Reference: Huang, T. ,Yang, G. ;  Tang, G.. "A fast two-dimensional median filtering algorithm",
+IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18.
+
+input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit),
+8 bit images are casted in 16 bit
+the number of histogram bins is determined from the maximum value present in the image
+
+The pixel neighborhood is defined by:
+
+* the given structuring element
+
+* an interval [g-s0,g+s1] in gray level around g the processed pixel gray level
+
+The kernel is flat (i.e. each pixel belonging to the neighborhood contributes equally)
+
+result image is 16 bit with respect to the input image
 
 """
 
-
-import warnings
 from skimage import img_as_ubyte
 
 import numpy as np
+from skimage.filter.rank import _crank16_bilateral
 
-from generic import find_bitdepth
-import _crank16_bilateral
+from skimage.filter.rank.generic import find_bitdepth
 
 
 __all__ = ['bilateral_mean', 'bilateral_pop']
@@ -67,7 +82,7 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -131,7 +146,7 @@ def bilateral_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fals
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
diff --git a/skimage/rank/generic.py b/skimage/filter/rank/generic.py
similarity index 100%
rename from skimage/rank/generic.py
rename to skimage/filter/rank/generic.py
diff --git a/skimage/rank/local/demo_all.py b/skimage/filter/rank/local/demo_all.py
similarity index 98%
rename from skimage/rank/local/demo_all.py
rename to skimage/filter/rank/local/demo_all.py
index 8df5c3e7..038c749b 100644
--- a/skimage/rank/local/demo_all.py
+++ b/skimage/filter/rank/local/demo_all.py
@@ -1,10 +1,9 @@
-import numpy as np
 import matplotlib.pyplot as plt
 from pprint import pprint
 
 from skimage import data
 from skimage.morphology.selem import disk
-import skimage.rank as rank
+import skimage.filter.rank as rank
 
 def plot_all():
     a8 = data.camera()
diff --git a/skimage/rank/local/demo_single.py b/skimage/filter/rank/local/demo_single.py
similarity index 92%
rename from skimage/rank/local/demo_single.py
rename to skimage/filter/rank/local/demo_single.py
index b601b226..39b9acc0 100644
--- a/skimage/rank/local/demo_single.py
+++ b/skimage/filter/rank/local/demo_single.py
@@ -1,10 +1,9 @@
 import numpy as np
 import matplotlib.pyplot as plt
-from pprint import pprint
 
 from skimage import data
 from skimage.morphology.selem import disk
-import skimage.rank as rank
+import skimage.filter.rank as rank
 
 
 if __name__ == '__main__':
diff --git a/skimage/filter/rank/local/iko_pan_Ja1.tif b/skimage/filter/rank/local/iko_pan_Ja1.tif
new file mode 100644
index 00000000..47201695
Binary files /dev/null and b/skimage/filter/rank/local/iko_pan_Ja1.tif differ
diff --git a/skimage/rank/local/test_morph_contr_enh.py b/skimage/filter/rank/local/test_morph_contr_enh.py
similarity index 93%
rename from skimage/rank/local/test_morph_contr_enh.py
rename to skimage/filter/rank/local/test_morph_contr_enh.py
index 812e81c5..f2f0f7c9 100644
--- a/skimage/rank/local/test_morph_contr_enh.py
+++ b/skimage/filter/rank/local/test_morph_contr_enh.py
@@ -3,7 +3,7 @@ import matplotlib.pyplot as plt
 import gdal
 
 from skimage.morphology import disk
-import skimage.rank as rank
+import skimage.filter.rank as rank
 
 filename = 'iko_pan_Ja1.tif'
 im16 = gdal.Open(filename).ReadAsArray().astype(np.uint16)
diff --git a/skimage/rank/local/test_rank.py b/skimage/filter/rank/local/test_rank.py
similarity index 95%
rename from skimage/rank/local/test_rank.py
rename to skimage/filter/rank/local/test_rank.py
index 75dd7f65..09cfdcd5 100644
--- a/skimage/rank/local/test_rank.py
+++ b/skimage/filter/rank/local/test_rank.py
@@ -3,7 +3,7 @@ import matplotlib.pyplot as plt
 
 from skimage import data
 from skimage.morphology.selem import disk
-import skimage.rank as rank
+import skimage.filter.rank as rank
 
 print dir(rank)
 
diff --git a/skimage/rank/percentile_rank.py b/skimage/filter/rank/percentile_rank.py
similarity index 98%
rename from skimage/rank/percentile_rank.py
rename to skimage/filter/rank/percentile_rank.py
index ac19ccd6..7908f5ac 100644
--- a/skimage/rank/percentile_rank.py
+++ b/skimage/filter/rank/percentile_rank.py
@@ -14,14 +14,11 @@ result image is 8 or 16 bit with respect to the input image
 
 """
 
-
-import warnings
 from skimage import img_as_ubyte
 import numpy as np
 
-from generic import find_bitdepth
-import _crank16_percentiles
-import _crank8_percentiles
+from skimage.filter.rank.generic import find_bitdepth
+from skimage.filter.rank import _crank16_percentiles, _crank8_percentiles
 
 __all__ = ['percentile_autolevel', 'percentile_gradient',
            'percentile_mean', 'percentile_mean_substraction',
@@ -77,7 +74,7 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -141,7 +138,7 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_
     to be updated
     >>> # Local gradient
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -205,7 +202,7 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -269,7 +266,7 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals
     to be updated
     >>> # Local mean_substraction
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -333,7 +330,7 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False,
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -397,7 +394,7 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False,
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 128*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -462,7 +459,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -526,7 +523,7 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
diff --git a/skimage/rank/rank.py b/skimage/filter/rank/rank.py
similarity index 98%
rename from skimage/rank/rank.py
rename to skimage/filter/rank/rank.py
index 51854128..0a9dee27 100644
--- a/skimage/rank/rank.py
+++ b/skimage/filter/rank/rank.py
@@ -12,14 +12,11 @@ result image is 8 or 16 bit with respect to the input image
 
 """
 
-
-import warnings
 from skimage import img_as_ubyte
 import numpy as np
+from skimage.filter.rank import _crank8, _crank16
 
-from generic import find_bitdepth
-import _crank16
-import _crank8
+from skimage.filter.rank.generic import find_bitdepth
 
 __all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum', 'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat']
 
@@ -71,7 +68,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -133,7 +130,7 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -194,7 +191,7 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -255,7 +252,7 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     to be updated
     >>> # Local gradient
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -317,7 +314,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     to be updated
     >>> # Local maximum
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 0, 0, 0, 0],
     ...                           [0, 0, 1, 0, 0],
@@ -379,7 +376,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -441,7 +438,7 @@ def meansubstraction(image, selem, out=None, mask=None, shift_x=False, shift_y=F
     to be updated
     >>> # Local meansubstraction
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -503,7 +500,7 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     to be updated
     >>> # Local median
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 0, 1, 0],
@@ -565,7 +562,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     to be updated
     >>> # Local minimum
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -628,7 +625,7 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     to be updated
     >>> # Local modal
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 5, 6, 0],
@@ -691,7 +688,7 @@ def morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -753,7 +750,7 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -815,7 +812,7 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
@@ -878,7 +875,7 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
     to be updated
     >>> # Local mean
     >>> from skimage.morphology import square
-    >>> import skimage.rank as rank
+    >>> import skimage.filter.rank as rank
     >>> ima8 = 255*np.array([[0, 0, 0, 0, 0],
     ...                           [0, 1, 1, 1, 0],
     ...                           [0, 1, 1, 1, 0],
diff --git a/skimage/rank/setup.py b/skimage/filter/rank/setup.py
similarity index 100%
rename from skimage/rank/setup.py
rename to skimage/filter/rank/setup.py
diff --git a/skimage/rank/tests/test_suite.py b/skimage/filter/rank/tests/test_suite.py
similarity index 97%
rename from skimage/rank/tests/test_suite.py
rename to skimage/filter/rank/tests/test_suite.py
index 9d2bcfea..4f1e6f81 100644
--- a/skimage/rank/tests/test_suite.py
+++ b/skimage/filter/rank/tests/test_suite.py
@@ -1,12 +1,12 @@
 import unittest
 
 import numpy as np
+from skimage.filter import rank
 
-from skimage.rank import _crank8,_crank8_percentiles
-from skimage.rank import _crank16,_crank16_bilateral,_crank16_percentiles
-from skimage.morphology import cmorph,disk
 from skimage import data
-from skimage import rank
+from skimage.morphology import cmorph,disk
+from skimage.filter.rank import _crank8, _crank16
+from skimage.filter.rank import _crank16_percentiles
 
 
 class TestSequenceFunctions(unittest.TestCase):
diff --git a/skimage/rank/local/__init__.py b/skimage/rank/local/__init__.py
deleted file mode 100644
index e69de29b..00000000