Some changes based on feedback from discussion on Github.

skimage/segmentation/_slic.pyx

@@ -16,7 +16,7 @@ def _slic_cython(double[:, :, :, ::1] image_zyx,
                  float step,
                  Py_ssize_t max_iter,
                  double[::1] spacing,
-                 bint slic_zero,
+                 bint slic_zero
                  ):
     """Helper function for SLIC segmentation.
 
@@ -177,8 +177,8 @@ def _slic_cython(double[:, :, :, ::1] image_zyx,
                         dist_color = 0
 
                         for c in range(3, n_features):
-                            dist_color += (image_zyx[z, y, x, c - 3]
-                                           - segments[k, c]) ** 2
+                            dist_color += (image_zyx[z, y, x, c - 3] -
+                                           segments[k, c]) ** 2
                         
                         # The reference implementation seems to only change
                         # the color if it increases from previous iteration

skimage/segmentation/slic_superpixels.py

@@ -59,7 +59,8 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
         Proportion of the maximum connected segment size. A value of 3 works
         in most of the cases.
     slic_zero: bool, optional
-        True to run SLIC-zero, False to run original SLIC.
+        Run SLIC-zero, the zero-parameter mode of SLIC
+
     Returns
     -------
     labels : 2D or 3D array
@@ -169,8 +170,8 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
     segments = np.concatenate([segments_z[..., np.newaxis],
                                segments_y[..., np.newaxis],
                                segments_x[..., np.newaxis],
-                               segments_color
-                               ], axis=-1).reshape(-1, 3 + image.shape[3])
+                               segments_color],
+                              axis=-1).reshape(-1, 3 + image.shape[3])
     segments = np.ascontiguousarray(segments)
 
     # we do the scaling of ratio in the same way as in the SLIC paper
@@ -178,10 +179,7 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
     step = float(max((step_z, step_y, step_x)))
     ratio = 1.0 / compactness
 
-    if slic_zero:
-        image = np.ascontiguousarray(image * ratio)
-    else:
-        image = np.ascontiguousarray(image * ratio)
+    image = np.ascontiguousarray(image * ratio)
 
     labels = _slic_cython(image, segments, step, max_iter, spacing, slic_zero)