diff --git a/scikits/image/transform/_hough_transform.pyx b/scikits/image/transform/_hough_transform.pyx
index 5d0d8b92..eeb4bb0d 100644
--- a/scikits/image/transform/_hough_transform.pyx
+++ b/scikits/image/transform/_hough_transform.pyx
@@ -86,7 +86,7 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
     cdef np.ndarray[ndim=2, dtype=np.int64_t] accum
     cdef np.ndarray[ndim=2, dtype=np.uint8_t] mask = np.zeros((height, width), dtype=np.uint8)
     cdef np.ndarray[ndim=2, dtype=np.int32_t] line_end = np.zeros((2, 2), dtype=np.int32)
-    cdef int max_distance, offset, num_indexes, index, other_max, settle
+    cdef int max_distance, offset, num_indexes, index
     cdef double a, b
     cdef int nidxs, nthetas, i, j, x, y, px, py, accum_idx, value, max_value, max_theta
     cdef int shift = 16
@@ -111,8 +111,6 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
     # create mask of all non-zero indexes
     for i in range(num_indexes):
         mask[y_idxs[i], x_idxs[i]] = 1
-    settle = 0
-    print
     while 1:
         # select random non-zero point
         count = len(points)
@@ -125,33 +123,19 @@ def _probabilistic_hough(np.ndarray img, int value_threshold, int line_length, \
         # if previously eliminated, skip
         if not mask[y, x]:
             continue
-        print "select", y, x
         value = 0
         max_value = value_threshold-1
         max_theta = -1
-        other_max = 0
         # apply hough transform on point
         for j in range(nthetas):
             accum_idx = <int>round((ctheta[j] * x + stheta[j] * y)) + offset
             accum[accum_idx, j] += 1
             value = accum[accum_idx, j]
             if value > max_value:
-                print "max", j, value
                 max_value = value
                 max_theta = j
-            # if two bordering angles have max values, extend the threshold for a while
-            if j != max_theta and value == max_value:
-                if abs(j - max_theta) <= 2:
-                    if settle > 100:
-                        other_max = 0
-                    else:
-                        other_max = 1
-            if j == 134 or j == 135:
-                print j, value, accum_idx, ctheta[j], stheta[j]
-        if max_value < value_threshold or other_max:
+        if max_value < value_threshold:
             continue
-        settle = 0
-        print "THETA", max_theta, max_value
         # from the random point walk in opposite directions and find line beginning and end
         a = -stheta[max_theta]
         b = ctheta[max_theta]
diff --git a/scikits/image/transform/tests/test_hough_transform.py b/scikits/image/transform/tests/test_hough_transform.py
index 7582035e..3d185521 100644
--- a/scikits/image/transform/tests/test_hough_transform.py
+++ b/scikits/image/transform/tests/test_hough_transform.py
@@ -13,6 +13,9 @@ def append_desc(func, description):
 
     return func
 
+from scikits.image.transform import *
+import math
+
 def test_hough():
     # Generate a test image
     img = np.zeros((100, 100), dtype=int)
@@ -28,6 +31,7 @@ def test_hough():
     assert_equal(dist > 70, dist < 72)
     assert_equal(theta > 0.78, theta < 0.79)
 
+
 def test_hough_angles():
     img = np.zeros((10, 10))
     img[0, 0] = 1
@@ -50,17 +54,18 @@ def test_probabilistic_hough():
     for i in range(25, 75):
         img[100 - i, i] = 100
         img[i, i] = 100
-    # test the line extraction a few times
-    for i in range(100):
-        lines = probabilistic_hough(img, threshold=10, line_length=10, line_gap=1)
-        # sort the lines according to the x-axis
-        sorted_lines = []
-        for line in lines:
-            line = list(line)
-            line.sort(lambda x,y: cmp(x[0], y[0]))
-            sorted_lines.append(line)
-        assert([(25, 75), (74, 26)] in sorted_lines)
-        assert([(25, 25), (74, 74)] in sorted_lines)
+    # decrease default theta sampling because similar orientations may confuse
+    # as mentioned in article of Galambos et al
+    theta=np.linspace(0, math.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 = []
+    for line in lines:
+        line = list(line)
+        line.sort(lambda x,y: cmp(x[0], y[0]))
+        sorted_lines.append(line)
+    assert([(25, 75), (74, 26)] in sorted_lines)
+    assert([(25, 25), (74, 74)] in sorted_lines)
 
 
 if __name__ == "__main__":