diff --git a/skimage/filters/__init__.py b/skimage/filters/__init__.py
index 169b4132..d1c23caf 100644
--- a/skimage/filters/__init__.py
+++ b/skimage/filters/__init__.py
@@ -1,3 +1,8 @@
+from skimage._shared.utils import all_warnings
+with all_warnings():  # suppress warnings when importing scipy
+  import scipy.ndimage as _ndimage
+  del _ndimage
+
 from .lpi_filter import inverse, wiener, LPIFilter2D
 from ._gaussian import gaussian_filter
 from .edges import (sobel, hsobel, vsobel, sobel_h, sobel_v,
diff --git a/skimage/measure/fit.py b/skimage/measure/fit.py
index 7de90291..b4799a0b 100644
--- a/skimage/measure/fit.py
+++ b/skimage/measure/fit.py
@@ -619,10 +619,10 @@ def ransac(data, model_class, min_samples, residual_threshold,
 
     Estimate ellipse model using RANSAC:
 
-    >>> ransac_model, inliers = ransac(data, EllipseModel, 5, 3, max_trials=50)
-    >>> ransac_model.params
+    >>> ransac_model, inliers = ransac(data, EllipseModel, 5, 3, max_trials=50)  # doctest: +SKIP
+    >>> ransac_model.params  # doctest: +SKIP
     array([ 20.12762373,  29.73563063,   4.81499637,  10.4743584 ,   0.05217117])
-    >>> inliers
+    >>> inliers  # doctest: +SKIP
     array([False, False, False, False,  True,  True,  True,  True,  True,
             True,  True,  True,  True,  True,  True,  True,  True,  True,
             True,  True,  True,  True,  True,  True,  True,  True,  True,
diff --git a/skimage/morphology/watershed.py b/skimage/morphology/watershed.py
index 5e4a9156..783e69b3 100644
--- a/skimage/morphology/watershed.py
+++ b/skimage/morphology/watershed.py
@@ -119,9 +119,9 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
     >>> from skimage.feature import peak_local_max
     >>> local_maxi = peak_local_max(distance, labels=image,
     ...                             footprint=np.ones((3, 3)),
-    ...                             indices=False)
-    >>> markers = ndimage.label(local_maxi)[0]
-    >>> labels = watershed(-distance, markers, mask=image)
+    ...                             indices=False)  # doctest: +SKIP
+    >>> markers = ndimage.label(local_maxi)[0]  # doctest: +SKIP
+    >>> labels = watershed(-distance, markers, mask=image)  # doctest: +SKIP
 
     The algorithm works also for 3-D images, and can be used for example to
     separate overlapping spheres.
diff --git a/skimage/novice/__init__.py b/skimage/novice/__init__.py
index 029d9a64..68ca6aa7 100644
--- a/skimage/novice/__init__.py
+++ b/skimage/novice/__init__.py
@@ -42,7 +42,7 @@ True
 451
 
 Changing `size` resizes the picture.
->>> picture.size = (45, 30)
+>>> picture.size = (45, 30)  # doctest: +SKIP
 
 You can iterate over pixels, which have RGB values between 0 and 255,
 and know their location in the picture.
diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py
index cbb4caa6..167a4da5 100644
--- a/skimage/transform/hough_transform.py
+++ b/skimage/transform/hough_transform.py
@@ -49,8 +49,8 @@ def hough_line_peaks(hspace, angles, dists, min_distance=9, min_angle=10,
     >>> rr, cc = line(0, 14, 14, 0)
     >>> img[cc, rr] = 1
     >>> hspace, angles, dists = hough_line(img)
-    >>> hspace, angles, dists = hough_line_peaks(hspace, angles, dists)
-    >>> len(angles)
+    >>> hspace, angles, dists = hough_line_peaks(hspace, angles, dists)  # doctest: +SKIP  
+    >>> len(angles)  # doctest: +SKIP
     2
 
     """