diff --git a/invesalius/data/watershed_process.py b/invesalius/data/watershed_process.py
new file mode 100644
index 0000000..41ff61b
--- /dev/null
+++ b/invesalius/data/watershed_process.py
@@ -0,0 +1,43 @@
+import numpy as np
+from scipy import ndimage
+from scipy.ndimage import watershed_ift, generate_binary_structure
+from skimage.morphology import watershed
+from skimage import filter
+
+
+def get_LUT_value(data, window, level):
+    return np.piecewise(data, 
+                        [data <= (level - 0.5 - (window-1)/2),
+                         data > (level - 0.5 + (window-1)/2)],
+                        [0, 255, lambda data: ((data - (level - 0.5))/(window-1) + 0.5)*(255-0)])
+
+
+
+def do_watershed(image, markers,  tfile, shape, bstruct, algorithm, mg_size, use_ww_wl, wl, ww, q):
+    mask = np.memmap(tfile, shape=shape, dtype='uint8', mode='r+')
+                
+    if use_ww_wl:
+        if algorithm == 'Watershed':
+            tmp_image = ndimage.morphological_gradient(
+                           get_LUT_value(image, ww, wl).astype('uint16'),
+                           mg_size)
+            tmp_mask = watershed(tmp_image, markers.astype('int16'), bstruct)
+        else:
+            tmp_image = get_LUT_value(image, ww, wl).astype('uint16')
+            #tmp_image = ndimage.gaussian_filter(tmp_image, self.config.mg_size)
+            #tmp_image = ndimage.morphological_gradient(
+                           #get_LUT_value(image, ww, wl).astype('uint16'),
+                           #self.config.mg_size)
+            tmp_mask = watershed_ift(tmp_image, markers.astype('int16'), bstruct)
+    else:
+        if algorithm == 'Watershed':
+            tmp_image = ndimage.morphological_gradient((image - image.min()).astype('uint16'), mg_size)
+            tmp_mask = watershed(tmp_image, markers.astype('int16'), bstruct)
+        else:
+            tmp_image = (image - image.min()).astype('uint16')
+            #tmp_image = ndimage.gaussian_filter(tmp_image, self.config.mg_size)
+            #tmp_image = ndimage.morphological_gradient((image - image.min()).astype('uint16'), self.config.mg_size)
+            tmp_mask = watershed_ift(tmp_image, markers.astype('int8'), bstruct)
+    mask[:] = tmp_mask
+    mask.flush()
+    q.put(1)
--
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