diff --git a/doc/examples/plot_local_binary_pattern.py b/doc/examples/plot_local_binary_pattern.py
index 85fd5b95..5429df3a 100644
--- a/doc/examples/plot_local_binary_pattern.py
+++ b/doc/examples/plot_local_binary_pattern.py
@@ -12,8 +12,8 @@ each other using the Kullback-Leibler-Divergence.
 import numpy as np
 import matplotlib
 import matplotlib.pyplot as plt
-import scipy.ndimage as nd
-import skimage.feature as ft
+from skimage.transform import rotate
+from skimage.feature import local_binary_pattern
 from skimage import data
 
 
@@ -34,7 +34,7 @@ def kullback_leibler_divergence(p, q):
 def match(refs, img):
     best_score = 10
     best_name = None
-    lbp = ft.local_binary_pattern(img, P, R, METHOD)
+    lbp = local_binary_pattern(img, P, R, METHOD)
     hist, _ = np.histogram(lbp, normed=True, bins=P + 2, range=(0, P + 2))
     for name, ref in refs.items():
         ref_hist, _ = np.histogram(ref, normed=True, bins=P + 2,
@@ -51,19 +51,19 @@ grass = data.load('grass.png')
 wall = data.load('rough-wall.png')
 
 refs = {
-    'brick': ft.local_binary_pattern(brick, P, R, METHOD),
-    'grass': ft.local_binary_pattern(grass, P, R, METHOD),
-    'wall': ft.local_binary_pattern(wall, P, R, METHOD)
+    'brick': local_binary_pattern(brick, P, R, METHOD),
+    'grass': local_binary_pattern(grass, P, R, METHOD),
+    'wall': local_binary_pattern(wall, P, R, METHOD)
 }
 
 # classify rotated textures
 print 'Rotated images matched against references using LBP:'
 print 'original: brick, rotated: 30deg, match result:',
-print match(refs, nd.rotate(brick, angle=30, reshape=False))
+print match(refs, rotate(brick, angle=30, resize=False))
 print 'original: brick, rotated: 70deg, match result:',
-print match(refs, nd.rotate(brick, angle=70, reshape=False))
+print match(refs, rotate(brick, angle=70, resize=False))
 print 'original: grass, rotated: 145deg, match result:',
-print match(refs, nd.rotate(grass, angle=145, reshape=False))
+print match(refs, rotate(grass, angle=145, resize=False))
 
 # plot histograms of LBP of textures
 fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(nrows=2, ncols=3,
diff --git a/doc/examples/plot_radon_transform.py b/doc/examples/plot_radon_transform.py
index efd8a776..adca8162 100644
--- a/doc/examples/plot_radon_transform.py
+++ b/doc/examples/plot_radon_transform.py
@@ -22,11 +22,11 @@ import matplotlib.pyplot as plt
 
 from skimage.io import imread
 from skimage import data_dir
-from skimage.transform import radon, iradon
-from scipy.ndimage import zoom
+from skimage.transform import radon, iradon, rescale
+
 
 image = imread(data_dir + "/phantom.png", as_grey=True)
-image = zoom(image, 0.4)
+image = rescale(image, scale=0.4)
 
 plt.figure(figsize=(8, 8.5))
 
diff --git a/doc/examples/plot_regionprops.py b/doc/examples/plot_regionprops.py
index 90b40a89..5a8ea01c 100644
--- a/doc/examples/plot_regionprops.py
+++ b/doc/examples/plot_regionprops.py
@@ -13,23 +13,15 @@ import numpy as np
 from skimage.draw import ellipse
 from skimage.morphology import label
 from skimage.measure import regionprops
-from scipy.ndimage import geometric_transform
+from skimage.transform import rotate
 
 
-ANGLE = 0.2
-
-def rotate(xy):
-    x, y = xy
-    out_x = math.cos(ANGLE) * x - math.sin(ANGLE) * y
-    out_y = math.sin(ANGLE) * x + math.cos(ANGLE) * y
-    return (out_x, out_y)
-
-image = np.zeros((600, 600), 'int')
+image = np.zeros((600, 600))
 
 rr, cc = ellipse(300, 350, 100, 220)
 image[rr,cc] = 1
 
-image = geometric_transform(image, rotate)
+image = rotate(image, angle=15, order=0)
 
 label_img = label(image)
 props = regionprops(label_img, [