Add spacing descriptions; use np.double

Implement @ahojnnes's comments on pull request. Use `np.double` as
dtype for arrays because in Cython, `float` is not `np.double`. And
add further clarification about the `spacing` parameter.

skimage/segmentation/_slic.pyx

@@ -25,6 +25,9 @@ def _slic_cython(double[:, :, :, ::1] image_zyx,
     max_iter : int
         The maximum number of k-means iterations.
     spacing : 1D array of double, shape (3,)
+        The voxel spacing along each image dimension. This parameter
+        controls the weights of the distances along z, y, and x during
+        k-means clustering.
 
     Returns
     -------

skimage/segmentation/slic_superpixels.py

@@ -34,6 +34,8 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
     spacing : (3,) array-like of floats, optional
         The voxel spacing along each image dimension. By default, `slic`
         assumes uniform spacing (same voxel resolution along z, y and x).
+        This parameter controls the weights of the distances along z, y,
+        and x during k-means clustering.
     multichannel : bool, optional
         Whether the last axis of the image is to be interpreted as multiple
         channels or another spatial dimension.
@@ -61,6 +63,11 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
     If `sigma > 0`, the image is smoothed using a Gaussian kernel prior to
     segmentation.
 
+    If `sigma > 0` and `spacing` is provided, the kernel width is divided
+    along each dimension by the spacing. For example, if `sigma=1` and
+    `spacing=[5, 1, 1]`, the effective `sigma` is `[0.2, 1, 1]`. This
+    ensures sensible smoothing for anisotropic images.
+
     The image is rescaled to be in [0, 1] prior to processing.
 
     Images of shape (M, N, 3) are interpreted as 2D RGB images by default. To
@@ -108,14 +115,14 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
 
     if spacing is None:
         spacing = np.ones(3)
-    elif type(spacing) in [list, tuple]:
-        spacing = np.array(spacing, float)
+    elif isinstance(spacing, (list, tuple)):
+        spacing = np.array(spacing, np.double)
     if not isinstance(sigma, coll.Iterable):
-        sigma = np.array([sigma, sigma, sigma], float)
-    elif type(sigma) in [list, tuple]:
-        sigma = np.array(sigma, float)
+        sigma = np.array([sigma, sigma, sigma], np.double)
+    elif isinstance(spacing, (list, tuple)):
+        sigma = np.array(sigma, np.double)
     if (sigma > 0).any():
-        sigma /= spacing.astype(float)
+        sigma /= spacing.astype(np.double)
         sigma = list(sigma) + [0]
         image = ndimage.gaussian_filter(image, sigma)