diff --git a/alibi/explainers/anchor_image.py b/alibi/explainers/anchor_image.py
index 2a734c164..9ff6f0e9f 100644
--- a/alibi/explainers/anchor_image.py
+++ b/alibi/explainers/anchor_image.py
@@ -221,13 +221,12 @@ def perturbation(
         segments_mask[:, anchor] = 1
 
         # for each sample, need to sample one of the background images if provided
-        if self.images_background:
+        if self.images_background is not None:
             backgrounds = np.random.choice(
                 range(len(self.images_background)),
                 segments_mask.shape[0],
                 replace=True,
             )
-            segments_mask = np.hstack((segments_mask, backgrounds.reshape(-1, 1)))
         else:
             backgrounds = [None] * segments_mask.shape[0]
             # create fudged image where the pixel value in each superpixel is set to the
@@ -247,13 +246,11 @@ def perturbation(
             mask = np.zeros(segments.shape).astype(bool)
             for superpixel in to_perturb:
                 mask[segments == superpixel] = True
-            if background_idx:
+            if background_idx is not None:
                 # replace values with those of background image
-                # TODO: Could images_background be None herre?
                 temp[mask] = self.images_background[background_idx][mask]
             else:
                 # ... or with the averaged superpixel value
-                # TODO: Where is fudged_image defined?
                 temp[mask] = fudged_image[mask]
             pert_imgs.append(temp)
 
diff --git a/alibi/explainers/tests/test_anchor_image.py b/alibi/explainers/tests/test_anchor_image.py
index 9cd50a852..4826dcddc 100644
--- a/alibi/explainers/tests/test_anchor_image.py
+++ b/alibi/explainers/tests/test_anchor_image.py
@@ -102,19 +102,22 @@ def test_sampler(predict_fn, models, mnist_data):
                          indirect=True,
                          ids='models={}'.format
                          )
-def test_anchor_image(predict_fn, models, mnist_data):
+@pytest.mark.parametrize('images_background', [True, False], ids='images_background={}'.format)
+def test_anchor_image(predict_fn, models, mnist_data, images_background):
     x_train = mnist_data["X_train"]
     image = x_train[0]
 
     segmentation_fn = "slic"
     segmentation_kwargs = {"n_segments": 10, "compactness": 10, "sigma": 0.5}
     image_shape = (28, 28, 1)
+    images_background = x_train[:10] if images_background else None
 
     explainer = AnchorImage(
         predict_fn,
         image_shape,
         segmentation_fn=segmentation_fn,
         segmentation_kwargs=segmentation_kwargs,
+        images_background=images_background
     )
 
     p_sample = 0.5  # probability of perturbing a superpixel