diff --git a/skimage/morphology/_skeletonize.py b/skimage/morphology/_skeletonize.py
index 13025c64..0fa80b54 100644
--- a/skimage/morphology/_skeletonize.py
+++ b/skimage/morphology/_skeletonize.py
@@ -84,14 +84,14 @@ def skeletonize(image):
     # look up table - there is one entry for each of the 2^8=256 possible
     # combinations of 8 binary neighbours. 1's, 2's and 3's are candidates
     # for removal at each iteration of the algorithm.
-    lut = [ 0,0,0,1,0,0,1,3,0,0,3,1,1,0,1,3,0,0,0,0,0,0,0,0,2,0,2,0,3,0,3,3,
-            0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,3,0,2,2,
-            0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
-            2,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,3,0,2,0,
-            0,0,3,1,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,
-            3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
-            2,3,1,3,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
-            2,3,0,1,0,0,0,1,0,0,0,0,0,0,0,0,3,3,0,1,0,0,0,0,2,2,0,0,2,0,0,0]
+    lut = [0,0,0,1,0,0,1,3,0,0,3,1,1,0,1,3,0,0,0,0,0,0,0,0,2,0,2,0,3,0,3,3,
+           0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,3,0,2,2,
+           0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+           2,0,0,0,0,0,0,0,2,0,0,0,2,0,0,0,3,0,0,0,0,0,0,0,3,0,0,0,3,0,2,0,
+           0,0,3,1,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,
+           3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+           2,3,1,3,0,0,1,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+           2,3,0,1,0,0,0,1,0,0,0,0,0,0,0,0,3,3,0,1,0,0,0,0,2,2,0,0,2,0,0,0]
 
     # convert to unsigned int (this should work for boolean values)
     skeleton = np.array(image).astype(np.uint8)
@@ -106,13 +106,13 @@ def skeletonize(image):
 
     # create the mask that will assign a unique value based on the
     #  arrangement of neighbouring pixels
-    mask = np.array([[1,  2,  4],
+    mask = np.array([[  1,  2,  4],
                      [128,  0,  8],
-                     [64, 32, 16]], np.uint8)
+                     [ 64, 32, 16]], np.uint8)
 
-    pixelRemoved = True
-    while pixelRemoved:
-        pixelRemoved = False
+    pixel_removed = True
+    while pixel_removed:
+        pixel_removed = False
 
         # assign each pixel a unique value based on its foreground neighbours
         neighbours = ndimage.correlate(skeleton, mask, mode='constant')
@@ -126,11 +126,11 @@ def skeletonize(image):
         # pass 1 - remove the 1's and 3's
         code_mask = (codes == 1)
         if np.any(code_mask):
-            pixelRemoved = True
+            pixel_removed = True
             skeleton[code_mask] = 0
         code_mask = (codes == 3)
         if np.any(code_mask):
-            pixelRemoved = True
+            pixel_removed = True
             skeleton[code_mask] = 0
 
         # pass 2 - remove the 2's and 3's
@@ -139,11 +139,11 @@ def skeletonize(image):
         codes = np.take(lut, neighbours)
         code_mask = (codes == 2)
         if np.any(code_mask):
-            pixelRemoved = True
+            pixel_removed = True
             skeleton[code_mask] = 0
         code_mask = (codes == 3)
         if np.any(code_mask):
-            pixelRemoved = True
+            pixel_removed = True
             skeleton[code_mask] = 0
 
     return skeleton.astype(bool)