minor doc changes

skimage/feature/_hessian_det_appx.pyx

@@ -22,7 +22,7 @@ cdef inline int  _clip(np.int_t x, np.int_t low, np.int_t high):
     Returns
     -------
     x : int
-        `x` clipped between 'high' and `low`.
+        `x` clipped between `high` and `low`.
 
     """
 
@@ -77,7 +77,7 @@ def _hessian_det_appx(np.ndarray[np.int_t, ndim=2] image, float sigma):
     """Computes the approximate Hessian Determinant over an image.
 
     This method uses box filters over integral images to compute the
-    approximate Hessian Determinant as described in [1].
+    approximate Hessian Determinant as described in [1]_.
 
     Parameters
     ----------

skimage/feature/blob.py

@@ -309,13 +309,12 @@ def blob_doh(image, min_sigma=1, max_sigma=30, num_sigma=10, threshold=500,
     Blobs are found using the Determinant of Hessian method [1]_. For each blob
     found, the method returns its coordinates and the standard deviation
     of the Gaussian Kernel used for the Hessian matrix whose determinant
-    detected the blob. Determinant of Hessians is approximated using [2]_
+    detected the blob. Determinant of Hessians is approximated using [2]_.
 
     Parameters
     ----------
     image : ndarray
-        Input grayscale image, blobs are assumed to be light on dark
+        Input grayscale image.Blobs can either be light on dark or vice versa.
-        background (white on black).
     min_sigma : float, optional
         The minimum standard deviation for Gaussian Kernel used to compute
         Hessian matrix. Keep this low to detect smaller blobs.