diff --git a/skimage/measure/tests/test_fit.py b/skimage/measure/tests/test_fit.py
index 9bfb975e..d8e0056a 100644
--- a/skimage/measure/tests/test_fit.py
+++ b/skimage/measure/tests/test_fit.py
@@ -32,7 +32,8 @@ def test_line_model_estimate():
     model_est.estimate(data)
 
     # test whether estimated parameters almost equal original parameters
-    x = np.random.rand(100, 2)
+    random_state = np.random.RandomState(1234)
+    x = random_state.rand(100, 2)
     assert_almost_equal(model0.predict(x), model_est.predict(x), 1)
 
 
@@ -75,8 +76,8 @@ def test_line_modelND_estimate():
              10 * np.arange(-100,100)[...,np.newaxis] * model0.params[1])
 
     # add gaussian noise to data
-    np.random.seed(1234)
-    data = data0 + np.random.normal(size=data0.shape)
+    random_state = np.random.RandomState(1234)
+    data = data0 + random_state.normal(size=data0.shape)
 
     # estimate parameters of noisy data
     model_est = LineModelND()
@@ -130,8 +131,8 @@ def test_circle_model_estimate():
     data0 = model0.predict_xy(t)
 
     # add gaussian noise to data
-    np.random.seed(1234)
-    data = data0 + np.random.normal(size=data0.shape)
+    random_state = np.random.RandomState(1234)
+    data = data0 + random_state.normal(size=data0.shape)
 
     # estimate parameters of noisy data
     model_est = CircleModel()
@@ -172,8 +173,8 @@ def test_ellipse_model_estimate():
     data0 = model0.predict_xy(t)
 
     # add gaussian noise to data
-    np.random.seed(1234)
-    data = data0 + np.random.normal(size=data0.shape)
+    random_state = np.random.RandomState(1234)
+    data = data0 + random_state.normal(size=data0.shape)
 
     # estimate parameters of noisy data
     model_est = EllipseModel()
@@ -206,7 +207,8 @@ def test_ransac_shape():
     data0[outliers[2], :] = (-100, -10)
 
     # estimate parameters of corrupted data
-    model_est, inliers = ransac(data0, CircleModel, 3, 5, random_state=np.random.RandomState(1))
+    model_est, inliers = ransac(data0, CircleModel, 3, 5,
+                                random_state=1)
 
     # test whether estimated parameters equal original parameters
     assert_equal(model0.params, model_est.params)
@@ -230,7 +232,8 @@ def test_ransac_geometric():
     dst[outliers[2]] = (50, 50)
 
     # estimate parameters of corrupted data
-    model_est, inliers = ransac((src, dst), AffineTransform, 2, 20, random_state=random_state)
+    model_est, inliers = ransac((src, dst), AffineTransform, 2, 20,
+                                random_state=random_state)
 
     # test whether estimated parameters equal original parameters
     assert_almost_equal(model0.params, model_est.params)
@@ -238,22 +241,20 @@ def test_ransac_geometric():
 
 
 def test_ransac_is_data_valid():
-    np.random.seed(1)
 
     is_data_valid = lambda data: data.shape[0] > 2
     model, inliers = ransac(np.empty((10, 2)), LineModelND, 2, np.inf,
-                            is_data_valid=is_data_valid)
+                            is_data_valid=is_data_valid, random_state=1)
     assert_equal(model, None)
     assert_equal(inliers, None)
 
 
 def test_ransac_is_model_valid():
-    np.random.seed(1)
 
     def is_model_valid(model, data):
         return False
     model, inliers = ransac(np.empty((10, 2)), LineModelND, 2, np.inf,
-                            is_model_valid=is_model_valid)
+                            is_model_valid=is_model_valid, random_state=1)
     assert_equal(model, None)
     assert_equal(inliers, None)