--- a
+++ b/src/dicom_utils.py
@@ -0,0 +1,42 @@
+import numpy as np
+def transform_to_hu(medical_image):
+    intercept = medical_image.RescaleIntercept
+    slope = medical_image.RescaleSlope
+    image = medical_image.pixel_array
+    hu_image = image * slope + intercept
+
+    return hu_image
+
+def apply_windowing(ds, window_center, window_width, normalized=False, zero_centered=True):
+    
+    # Get the pixel data and rescale to Hounsfield units (HU)
+    pixel_array = ds.pixel_array.astype(np.float32)
+    intercept = ds.RescaleIntercept
+    slope = ds.RescaleSlope
+    hu_array = pixel_array * slope + intercept
+    
+    # Apply windowing
+    window_min = window_center - (window_width / 2)
+    window_max = window_center + (window_width / 2)
+    windowed_array = np.clip(hu_array, window_min, window_max)
+    if(zero_centered):
+        windowed_array = zero_center(windowed_array)
+    if(normalized):
+    # Normalize the windowed array to [0, 1]
+        normalized_array = (windowed_array - window_min) / (window_max - window_min)
+        return normalized_array
+    else: 
+        return windowed_array
+
+def set_window(img, hu=[-800.,1200.]):
+    window = np.array(hu)
+    newimg = (img-window[0]) / (window[1]-window[0])
+    newimg[newimg < 0] = 0
+    newimg[newimg > 1] = 1
+    newimg = (newimg * 255).astype('uint8')
+    return newimg
+
+
+def zero_center(hu_image):
+    hu_image = hu_image - np.mean(hu_image)
+    return hu_image
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