remove load_dcm_img function from dataset utils

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(
@@ -31,9 +9,17 @@ def align_images(
     """
     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
+    Parameters
+    ----------
+    image_1: np.ndarray
+        the image to be aligned
+    image_2: np.ndarray
+        the image to which image_1 is aligned
+
+    Returns
+    -------
+    Tuple[np.ndarray, np.ndarray]
+        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]:

mu_map/vis/hist2d.py

@@ -3,7 +3,8 @@ import matplotlib.pyplot as plt
 import numpy as np
 import pydicom
 
-from mu_map.dataset.util import load_dcm_img, align_images
+from mu_map.dataset.util import align_images
+from mu_map.file.dicom import load_dcm_img
 from mu_map.util import to_grayscale
 
 mu_map = load_dcm_img("./data/second/images/0085-stress-mu_map.dcm")