diff --git a/skimage/segmentation/__init__.py b/skimage/segmentation/__init__.py
index 36595240..8fa8dfb8 100644
--- a/skimage/segmentation/__init__.py
+++ b/skimage/segmentation/__init__.py
@@ -1,7 +1,5 @@
 from .random_walker_segmentation import random_walker
 from .felzenszwalb import felzenszwalb_segmentation
-from .felzenszwalb import felzenszwalb_segmentation_grey
 from .quickshift import quickshift
 
-__all__ = [random_walker, quickshift, felzenszwalb_segmentation,
-        felzenszwalb_segmentation_grey]
+__all__ = [random_walker, quickshift, felzenszwalb_segmentation]
diff --git a/skimage/segmentation/_felzenszwalb.pyx b/skimage/segmentation/_felzenszwalb.pyx
index afa39ad8..d058975d 100644
--- a/skimage/segmentation/_felzenszwalb.pyx
+++ b/skimage/segmentation/_felzenszwalb.pyx
@@ -1,12 +1,13 @@
 import numpy as np
 cimport numpy as np
-from collections import defaultdict
 import scipy
 
 from skimage.morphology.ccomp cimport find_root, join_trees
 
+from ..util import img_as_float
 
-def felzenszwalb_segmentation_grey(image, scale=200, sigma=0.8):
+
+def _felzenszwalb_segmentation_grey(image, scale=1, sigma=0.8):
     """Computes Felsenszwalb's efficient graph based segmentation for a single channel.
 
     Produces an oversegmentation of a 2d image using a fast, minimum spanning
@@ -26,7 +27,6 @@ def felzenszwalb_segmentation_grey(image, scale=200, sigma=0.8):
 
     scale: float
         Free parameter. Higher means larger clusters.
-        For 0-255 data, hundereds are good.
 
     sigma: float
         Width of Gaussian kernel used in preprocessing.
@@ -39,6 +39,7 @@ def felzenszwalb_segmentation_grey(image, scale=200, sigma=0.8):
     if image.ndim != 2:
         raise ValueError("This algorithm works only on single-channel 2d images."
                 "Got image of shape %s" % str(image.shape))
+    image = img_as_float(image)
     scale = float(scale)
     image = scipy.ndimage.gaussian_filter(image, sigma=sigma)
 
@@ -88,11 +89,12 @@ def felzenszwalb_segmentation_grey(image, scale=200, sigma=0.8):
             seg_new = find_root(segments_p, seg0)
             segment_size[seg_new] = segment_size[seg0] + segment_size[seg1]
             cint[seg_new] = costs_p[0]
-    
+
     # unravel the union find tree
     flat = segments.ravel()
     old = np.zeros_like(flat)
     while (old != flat).any():
         old = flat
         flat = flat[flat]
+    flat = np.unique(flat, return_inverse=True)[1]
     return flat.reshape((width, height))
diff --git a/skimage/segmentation/felzenszwalb.py b/skimage/segmentation/felzenszwalb.py
index b0467d57..be879e13 100644
--- a/skimage/segmentation/felzenszwalb.py
+++ b/skimage/segmentation/felzenszwalb.py
@@ -1,11 +1,11 @@
 import warnings
 import numpy as np
 
-from ._felzenszwalb import felzenszwalb_segmentation_grey
+from ._felzenszwalb import _felzenszwalb_segmentation_grey
 
 
-def felzenszwalb_segmentation(image, scale=200, sigma=0.8):
-    """Computes Felsenszwalb's segmentation for multi channel images.
+def felzenszwalb_segmentation(image, scale=1, sigma=0.8):
+    """Computes Felsenszwalb's efficient graph based image segmentation.
 
     Produces an oversegmentation of a multichannel (i.e. RGB) image
     using a fast, minimum spanning tree based clustering on the image grid.
@@ -47,7 +47,7 @@ def felzenszwalb_segmentation(image, scale=200, sigma=0.8):
     #image = img_as_float(image)
     if image.ndim == 2:
         # assume single channel image
-        return felzenszwalb_segmentation_grey(image, scale=scale, sigma=sigma)
+        return _felzenszwalb_segmentation_grey(image, scale=scale, sigma=sigma)
 
     elif image.ndim != 3:
         raise ValueError("Got image with ndim=%d, don't know"
@@ -62,13 +62,11 @@ def felzenszwalb_segmentation(image, scale=200, sigma=0.8):
     # compute quickshift for each channel
     for c in xrange(n_channels):
         channel = np.ascontiguousarray(image[:, :, c])
-        seg = felzenszwalb_segmentation_grey(channel, scale=scale, sigma=sigma)
-        segmentations.append(seg)
+        s = _felzenszwalb_segmentation_grey(channel, scale=scale, sigma=sigma)
+        segmentations.append(s)
 
     # put pixels in same segment only if in the same segment in all images
     # we do this by combining the channels to one number
-    segmentations = [np.unique(s, return_inverse=True)[1] for s in
-            segmentations]
     n0 = max(segmentations[0])
     n1 = max(segmentations[1])
     hasher = np.array([n1 * n0, n0, 1])