add util module to dataset

mu_map/dataset/util.py

+from typing import Tuple
+
+import numpy as np
+import pydicom
+
+"""
+Since DICOM images only allow images stored in short integer format,
+the Siemens scanner software multiplies values by a factor before storing
+so that no precision is lost.
+The scale can be found in this private DICOM tag.
+"""
+DCM_TAG_PIXEL_SCALE_FACTOR = 0x00331038
+
+
+def load_dcm_img(filename: str) -> np.ndarray:
+    """
+    Load a DICOM image as a numpy array and apply normalization of the Siemens SPECT/CT
+    Scanner.
+
+    :param filename: filename of the DICOM image
+    :return: the image scaled and loaded into a numpy array
+    """
+    image = pydicom.dcmread(filename)
+    image = image.pixel_array / image[DCM_TAG_PIXEL_SCALE_FACTOR].value
+    return image
+
+
+def align_images(
+    image_1: np.ndarray, image_2: np.ndarray
+) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Center align the image with more slices to the one with fewer slices on the first axis (z-axis).
+
+    :param image_1: the image to be aligned
+    :param image_2: the image to which image_1 is aligned
+    :return: both images aligned in the order they were put in
+    """
+    # reverse function if image_2 has more slices
+    if image_2.shape[0] > image_1.shape[0]:
+        return align_images(image_2, image_1)[::-1]
+
+    # central slice of image 2
+    c_2 = image_2.shape[0] // 2
+    # image to the left and right of the center
+    left = c_2
+    right = image_2.shape[0] - c_2
+
+    # central slice of image 1
+    c_1 = image_1.shape[0] // 2
+    # select center and same amount to the left/right as image_2
+    return image_1[(c_1 - left) : (c_1 + right)], image_2