refactor: handle multi-frame DICOM files by extracting the middle frame in get_PIL_image functions

helpers/pydicom_PIL.py

@@ -89,9 +89,13 @@ def get_PIL_image(dataset):
         raise TypeError("Cannot show image -- DICOM dataset does not have "
                         "pixel data")
 
+    # Handle multi-frame DICOM files by extracting the middle frame
+    pixel_array = dataset.pixel_array
+    if len(pixel_array.shape) == 3:
+        pixel_array = pixel_array[pixel_array.shape[0] // 2]
 
     # Apply modality lut
-    array = apply_modality_lut(dataset.pixel_array, dataset)
+    array = apply_modality_lut(pixel_array, dataset)
     # voi lut doesn't seem to help
     #array = apply_voi_lut(array, dataset)
 
@@ -115,7 +119,8 @@ def get_PIL_image(dataset):
         # PIL size = (width, height)
         size = (dataset.Columns, dataset.Rows)
 
-        array = dataset.pixel_array
+        # For multi-frame we already have the middle frame sliced as 2D in pixel_array
+        array = pixel_array
 
         #array = apply_modality_lut(array, dataset)
 
@@ -124,17 +129,11 @@ def get_PIL_image(dataset):
         #array = apply_windowing(array, dataset)
         array = lin_stretch_img(array, 0.1, 99.9)  # Apply "linear stretching" (lower percentile 0.1 goes to 0, and percentile 99.9 to 255).
 
-
         if dataset["PhotometricInterpretation"].value == "MONOCHROME1":
             # We need to invert
             array = np.invert(array)
 
         return PIL.Image.fromarray(array).convert('L')
-        pixel_data = array.tostring()
-        # Recommended to specify all details
-        # by http://www.pythonware.com/library/pil/handbook/image.htm
-        im = PIL.Image.frombuffer(mode, size, pixel_data,
-                                  "raw", mode, 0, 1)
     else:
         ew = dataset['WindowWidth']
         ec = dataset['WindowCenter']
@@ -159,6 +158,11 @@ def get_PIL_image2(dataset):
     if ('PixelData' not in dataset):
         raise TypeError("Cannot show image -- DICOM dataset does not have "
                         "pixel data")
+
+    pixel_array = dataset.pixel_array
+    if len(pixel_array.shape) == 3:
+        pixel_array = pixel_array[pixel_array.shape[0] // 2]
+
     # can only apply LUT if these window info exists
     if ('WindowWidth' not in dataset) or ('WindowCenter' not in dataset):
         bits = dataset.BitsAllocated
@@ -176,20 +180,14 @@ def get_PIL_image2(dataset):
             raise TypeError("Don't know PIL mode for %d BitsAllocated "
                             "and %d SamplesPerPixel" % (bits, samples))
 
-        # PIL size = (width, height)
+        im = PIL.Image.fromarray(pixel_array).convert('L')
-        size = (dataset.Columns, dataset.Rows)
-
-        # Recommended to specify all details
-        # by http://www.pythonware.com/library/pil/handbook/image.htm
-        im = PIL.Image.frombuffer(mode, size, dataset.PixelData,
-                                  "raw", mode, 0, 1)
 
     else:
         ew = dataset['WindowWidth']
         ec = dataset['WindowCenter']
         ww = int(ew.value[0] if ew.VM > 1 else ew.value)
         wc = int(ec.value[0] if ec.VM > 1 else ec.value)
-        image = get_LUT_value(dataset.pixel_array, ww, wc)
+        image = get_LUT_value(pixel_array, ww, wc)
         # Convert mode to L since LUT has only 256 values:
         #   http://www.pythonware.com/library/pil/handbook/image.htm
         im = PIL.Image.fromarray(image).convert('L')