diff --git a/scikits/image/transform/_hough_transform.pyx b/scikits/image/transform/_hough_transform.pyx
index eeb4bb0d..ddcf91f6 100644
--- a/scikits/image/transform/_hough_transform.pyx
+++ b/scikits/image/transform/_hough_transform.pyx
@@ -1,11 +1,13 @@
 cimport cython
-
 import numpy as np
 cimport numpy as np
 from random import randint
 np.import_array()
 
-
+cdef extern from "stdlib.h":
+    int rand()
+    void randomize()
+        
 cdef extern from "math.h":
     int abs(int)
     double fabs(double)
@@ -95,7 +97,6 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
     cdef int xflag, x0, y0, dx0, dy0, dx, dy, gap, x1, y1, good_line, count
     max_distance = 2 * <int>ceil((sqrt(img.shape[0] * img.shape[0] + 
                                        img.shape[1] * img.shape[1])))
-    #max_distance = (img.shape[0] + img.shape[1]) * 2 
     accum = np.zeros((max_distance, theta.shape[0]), dtype=np.int64)
     offset = max_distance / 2
     # find the nonzero indexes
@@ -103,7 +104,6 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
     y_idxs, x_idxs =  np.nonzero(img)
     num_indexes = y_idxs.shape[0] # x and y are the same shape
     nthetas = theta.shape[0]
-    
     points = []
     for i in range(num_indexes):
         points.append((x_idxs[i], y_idxs[i]))
@@ -116,7 +116,7 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
         count = len(points)
         if count == 0:
             break        
-        index = randint(0, count-1)
+        index = rand() % (count)
         x = points[index][0]
         y = points[index][1]
         del points[index]        
diff --git a/scikits/image/transform/hough_transform.py b/scikits/image/transform/hough_transform.py
index 45d44599..31ffa565 100644
--- a/scikits/image/transform/hough_transform.py
+++ b/scikits/image/transform/hough_transform.py
@@ -54,7 +54,7 @@ _py_hough = _hough
 
 # try to import and use the faster Cython version if it exists
 try:
-    from ._hough_transform import _hough  
+    from ._hough_transform import _hough
 except ImportError:
     pass
 
@@ -67,11 +67,16 @@ def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10, theta=No
     img : (M, N) ndarray
         Input image with nonzero values representing edges.
     value_threshold: int
-        Threshold
-    theta :1D ndarray, dtype=double
+        Threshold        
+    line_length: int, optional (default 50)
+        Minimum accepted length of detected lines.
+        Increase the parameter to extract longer lines.
+    line_gap: int, optional, (default 10)
+        Maximum gap between pixels to still form a line. 
+        Increase the parameter to merge broken lines more aggresively.
+    theta :1D ndarray, dtype=double, optional, default (-pi/2 .. pi/2)
         Angles at which to compute the transform, in radians.
-        Defaults to -pi/2 .. pi/2
-
+        
     Returns
     -------
     lines : list
diff --git a/scikits/image/transform/tests/test_hough_transform.py b/scikits/image/transform/tests/test_hough_transform.py
index 3d185521..175c1b37 100644
--- a/scikits/image/transform/tests/test_hough_transform.py
+++ b/scikits/image/transform/tests/test_hough_transform.py
@@ -14,7 +14,6 @@ def append_desc(func, description):
     return func
 
 from scikits.image.transform import *
-import math
 
 def test_hough():
     # Generate a test image
@@ -56,7 +55,7 @@ def test_probabilistic_hough():
         img[i, i] = 100
     # decrease default theta sampling because similar orientations may confuse
     # as mentioned in article of Galambos et al
-    theta=np.linspace(0, math.pi, 45)
+    theta=np.linspace(0, np.pi, 45)
     lines = probabilistic_hough(img, theta=theta, threshold=10, line_length=10, line_gap=1)
     # sort the lines according to the x-axis
     sorted_lines = []