diff --git a/tests/test_mask_expansion.py b/tests/test_mask_expansion.py
index 13d8f86..f10a5c7 100644
--- a/tests/test_mask_expansion.py
+++ b/tests/test_mask_expansion.py
@@ -1,5 +1,5 @@
 import numpy as np
-from decimer_segmentation import get_bounding_box_center
+from decimer_segmentation.complete_structure import *
 
 
 def test_get_bounding_box_center():
@@ -12,8 +12,121 @@ def test_get_bounding_box_center():
 
 
 def test_get_edge_line():
-    pass
+    # Return intercept and slop for a linear function between 2 points in 2D Space
+    test_linenode1 = [1, 6]
+    test_linenode2 = [5, 8]
+    expected_result = [0.5, 5.5]
+    actual_result = get_edge_line(test_linenode1, test_linenode2)
+    for index in range(len(expected_result)):
+        assert expected_result[index] == actual_result[index]
 
 
+def test_get_euklidian_distance():
+    # Calculates euklidian distance between two given points in 2D Space
+    test_distancepoint1 = [1, 6]
+    test_distancepoint2 = [5, 8]
+    expected_result = 4.47213595499958
+    actual_result = get_euklidian_distance(test_distancepoint1, test_distancepoint2)
+    assert expected_result == actual_result
+    
 def test_set_x_range():
+    # For the contour-based expansion, non-white pixels on the contours of the original polygon bounding box are detected
+    test_distance = 3
+    test_eukl_distance = 4
+    test_image_array = np.array([[1,5]])
+    expected_result = [2.5,  2.75, 1.,  0.25, 0.75, 0.,   2.,   2.25, 1.5,  1.75, 1.25, 0.5]
+    actual_result = set_x_range(test_distance, test_eukl_distance, test_image_array)
+    assert set(expected_result) == set(actual_result)
+
+def test_get_next_pixel_to_check():
+    # Returns the next pixel to check in the image
+    test_bounding_box = np.array([[1,5], [2,4]])
+    test_node_index = 1
+    test_step = 4
+    test_image_shape = [2,4,6]
+    expected_result = (3, 1)
+    actual_result = get_next_pixel_to_check(test_bounding_box, test_node_index, test_step, test_image_shape)
+    for index in range(len(expected_result)):
+        assert expected_result[index] == actual_result[index]
+
+def test_adapt_x_values():
+    # Returns a bounding box where the nodes are altered depending on their relative position to bounding box centre
+    test_bounding_box = np.array([[1,5], [2,4]])
+    test_node_index = 1
+    test_image_shape = [2,4,6]
+    expected_result = np.array([[1, 5], [3, 4]])
+    actual_result = adapt_x_values(test_bounding_box, test_node_index, test_image_shape)
+    assert expected_result.all() == actual_result.all()
+
+
+def test_binarize_image():
+    # Returns the binarized image (np.array) by applying the otsu threshold
+    #test_image_array = np.array([1,2,3])
+    #test_threshold = "otsu"
+    #expected_result = False
+    #actual_result = binarize_image(test_image_array, test_threshold)
+    #assert expected_result == actual_result
+    pass
+
+
+def test_get_biggest_polygon():
+    # returns the Polygon object that only contains the biggest bounding box
+    #test_polygon = np.array([[(7,7), (8,16)]])
+    #expected_result = np.array([[(7,7), (8,16)]])
+    #actual_result = get_biggest_polygon(test_polygon)
+    #assert expected_result.all() == actual_result.all()
+    pass
+
+
+def test_get_contour_seeds():
+    test_image_array = np.array([(1,2)])
+    test_bounding_box = np.array([[1,1], [2,1]])
+    expected_result = []
+    actual_result = get_contour_seeds(test_image_array, test_bounding_box)
+    for index in range(len(expected_result)):
+        assert expected_result[index] == actual_result[index]
+
+def test_get_mask_center():
+    test_mask_array = np.array([(9, 5, 9, 5)])
+    expected_result = (1, 0)
+    actual_result = get_mask_center(test_mask_array)
+    for index in range(len(expected_result)):
+        assert expected_result[index] == actual_result[index]
+
+
+def test_get_seeds():
+    test_image_array = np.array([(3,2)])
+    test_mask_array = np.array([(9, 5, 9)])
+    expected_result = []
+    actual_result = get_seeds(test_image_array, test_mask_array)
+    for index in range(len(expected_result)):
+        assert expected_result[index] == actual_result[index]
+
+
+def test_get_neighbour_pixels():
+    test_seed_pixel = [2,4]
+    test_image_shape = [9,2,4]
+    expected_result = [(1, 3), (1, 4), (1, 5)]
+    actual_result = get_neighbour_pixels(test_seed_pixel, test_image_shape)
+    for index in range(len(expected_result)):
+        assert expected_result[index] == actual_result[index]
+
+
+def test_expand_masks():
+    test_image_array = np.array([(3,2)])
+    test_seed_pixels = [(2,4)]
+    test_mask_array = np.array([(9, 5, 9)])
+    expected_result = np.array([(0. , 0. , 0.)])
+    actual_result = expand_masks(test_image_array, test_seed_pixels, test_mask_array)  
+    assert expected_result.all() == actual_result.all()
+
+
+def test_expansion_coordination():
+    test_mask_array = np.array([(9, 5, 9)])
+    test_image_array = np.array([(3,2)])
+    expected_result = np.array([(9, 5, 9)])
+    actual_result = expansion_coordination(test_mask_array, test_image_array)
+    assert expected_result.all() == actual_result.all()
+
+def test_complete_structure_mask():
     pass