diff --git a/keras_contrib/preprocessing/__init__.py b/keras_contrib/preprocessing/__init__.py
index e69de29..ca58033 100644
--- a/keras_contrib/preprocessing/__init__.py
+++ b/keras_contrib/preprocessing/__init__.py
@@ -0,0 +1,6 @@
+from __future__ import absolute_import
+from . import image_segmentation
+
+# Globally-importable preprocessing
+from .image_segmentation import SegDirectoryIterator
+from .image_segmentation import SegDataGenerator
diff --git a/keras_contrib/preprocessing/image_segmentation.py b/keras_contrib/preprocessing/image_segmentation.py
index b2de3d5..ce8aa9d 100644
--- a/keras_contrib/preprocessing/image_segmentation.py
+++ b/keras_contrib/preprocessing/image_segmentation.py
@@ -1,6 +1,10 @@
+""" Preprocessing for semantic image segmentation
+
+    adapted from: https://github.com/aurora95/Keras-FCN
+"""
 from keras.preprocessing.image import *
 from keras.applications.imagenet_utils import preprocess_input
-from keras import backend as K
+from .. import backend as K
 from PIL import Image
 import numpy as np
 import os
@@ -33,11 +37,11 @@ def pair_center_crop(x, y, center_crop_size, data_format, **kwargs):
     h_start, h_end = centerh - lh, centerh + rh
     w_start, w_end = centerw - lw, centerw + rw
     if data_format == 'channels_first':
-        return x[:, h_start:h_end, w_start:w_end], \
-               y[:, h_start:h_end, w_start:w_end]
+        return (x[:, h_start:h_end, w_start:w_end],
+                y[:, h_start:h_end, w_start:w_end])
     elif data_format == 'channels_last':
-        return x[h_start:h_end, w_start:w_end, :], \
-               y[h_start:h_end, w_start:w_end, :]
+        return (x[h_start:h_end, w_start:w_end, :],
+                y[h_start:h_end, w_start:w_end, :])
 
 
 def random_crop(x, random_crop_size, data_format, sync_seed=None, **kwargs):
diff --git a/tests/keras_contrib/preprocessing/test_image_segmentation.py b/tests/keras_contrib/preprocessing/test_image_segmentation.py
index 0c5fe0e..e1ad097 100644
--- a/tests/keras_contrib/preprocessing/test_image_segmentation.py
+++ b/tests/keras_contrib/preprocessing/test_image_segmentation.py
@@ -1,5 +1,6 @@
 from keras.preprocessing.image import img_to_array, array_to_img
-from utils import SegDataGenerator
+from keras_contrib.preprocessing.image_segmentation import SegDataGenerator
+from keras_contrib.preprocessing import image_segmentation
 from PIL import Image as PILImage
 import numpy as np
 
@@ -30,19 +31,31 @@ def test_pair_crop(crop_function):
     crop_height = img1.height / 5
 
     result1, result2 = crop_function(img_to_array(img1),
-        img_to_array(img2),
-        (crop_height, crop_width),
-        'channels_last')
+                                     img_to_array(img2),
+                                     (crop_height, crop_width),
+                                     'channels_last')
     result1 = array_to_img(result1)
     result2 = array_to_img(result2)
 
     assert result1.width == crop_width == result2.width
     assert result2.height == crop_height == result2.height
 
-test_center_crop = lambda: test_crop(SegDataGenerator.center_crop)
 
-test_random_crop = lambda: test_crop(SegDataGenerator.random_crop)
+def test_center_crop():
+    test_crop(image_segmentation.center_crop)
 
-test_pair_center_crop = lambda: test_pair_crop(SegDataGenerator.pair_center_crop)
 
-test_pair_random_crop = lambda: test_pair_crop(SegDataGenerator.pair_random_crop)
+def test_random_crop():
+    test_crop(image_segmentation.random_crop)
+
+
+def test_pair_center_crop():
+    test_pair_crop(image_segmentation.pair_center_crop)
+
+
+def test_pair_random_crop():
+    test_pair_crop(image_segmentation.pair_random_crop)
+
+
+def test_seg_data_generator():
+    datagen = SegDataGenerator()