diff --git a/scikits/image/transform/tests/test_radon_transform.py b/scikits/image/transform/tests/test_radon_transform.py
index a1bb64f8..6679528f 100644
--- a/scikits/image/transform/tests/test_radon_transform.py
+++ b/scikits/image/transform/tests/test_radon_transform.py
@@ -3,7 +3,7 @@ from numpy.testing import *
 from scikits.image.transform import *
 
 def rescale(x):
-    x = x.astype(float, copy=True)
+    x = x.astype(float)
     x -= x.min()
     x /= x.max()
     return x
@@ -30,7 +30,33 @@ def test_radon_iradon():
     reconstructed = iradon(radon(image), filter="ramp", interpolation="nearest")
     delta = np.mean(abs(image - reconstructed))
     assert delta < 0.05
-    
+   
+def test_iradon_angles():
+    """
+    Test with different number of projections
+    """
+    size = 100
+    # Synthetic data
+    image = np.tri(size) + np.tri(size)[::-1]
+    # Large number of projections: a good quality is expected
+    nb_angles = 200
+    radon_image_200 = radon(image, theta=np.linspace(0, 180, nb_angles, 
+                    endpoint=False))
+    reconstructed = iradon(radon_image_200)
+    delta_200 = np.mean(abs(rescale(image) - rescale(reconstructed)))
+    assert delta_200 < 0.03
+    # Lower number of projections
+    nb_angles = 80
+    radon_image_80 = radon(image, theta=np.linspace(0, 180, nb_angles,
+                    endpoint=False))
+    # Test whether the sum of all projections is approximately the same
+    s = radon_image_80.sum(axis=0)
+    assert np.allclose(s, s[0], rtol=0.01)
+    reconstructed = iradon(radon_image_80)
+    delta_80 = np.mean(abs(image/np.max(image) - reconstructed/np.max(reconstructed)))
+    # Loss of quality when the number of projections is reduced
+    assert delta_80 > delta_200
+
         
 if __name__ == "__main__":
     run_module_suite()