Update text to reflect removal of np.stack.

Author: rossbar

content/tutorial-x-ray-image-processing.md

@@ -89,7 +89,7 @@ each notebook cell.
 +++
 
 1. Examine an X-ray with `imageio`
-2. Combine images with `np.stack()` to demonstrate progression
+2. Combine images into a multi-dimensional array to demonstrate progression
 3. Edge detection using the Laplacian-Gaussian, Gaussian gradient, Sobel, and
    Canny filters
 4. Apply masks to X-rays with `np.where()`
@@ -140,47 +140,45 @@ plt.axis('off')
 plt.show()
 ```
 
-## Combine images with `np.stack()` to demonstrate progression
+## Combine images into a multidimensional array to demonstrate progression
 
 +++
 
-With NumPy's `np.stack()` you can combine multiple X-rays to make an
-n-dimensional array and then show the "health progress" in a sequential manner.
-
-In the next example, instead of 1 image you'll use 8 X-ray 1024x1024-pixel
+In the next example, instead of 1 image you'll use 9 X-ray 1024x1024-pixel
 images from the ChestX-ray8 dataset that have been downloaded and extracted
 from one of the dataset files. They are numbered from `...000.png` to
 `...008.png` and let's assume they belong to the same patient.
 
-+++
-
-**1.** Import NumPy, read in each of the X-rays, and stack them together with
-`np.stack()`:
+**1.** Import NumPy, read in each of the X-rays, and create a three-dimensional
+array where the first dimension corresponds to image number:
 
 ```{code-cell} ipython3
 import numpy as np
+num_imgs = 9
 
 combined_xray_images_1 = np.array(
-    [imageio.imread(os.path.join(DIR, f"00000011_00{i}.png")) for i in range(9)]
+    [imageio.imread(os.path.join(DIR, f"00000011_00{i}.png")) for i in range(num_imgs)]
 )
 ```
 
-**2.** Check the shape of the new X-ray image array containing 8 stacked images:
+**2.** Check the shape of the new X-ray image array containing 9 stacked images:
 
 ```{code-cell} ipython3
 combined_xray_images_1.shape
 ```
 
+Note that the shape in the first dimension matches `num_imgs`, so the
+`combined_xray_images_1` array can be interpreted as a stack of 2D images.
+
 **3.** You can now display the "health progress" by plotting each of frames next
 to each other using Matplotlib:
 
 ```{code-cell} ipython3
-fig, axes = plt.subplots(nrows=1, ncols=8, figsize=(30, 30))
+fig, axes = plt.subplots(nrows=1, ncols=num_imgs, figsize=(30, 30))
 
-for i in range(8):
-    x = combined_xray_images_1[i]
-    axes[i].imshow(x, cmap='gray')
-    axes[i].axis('off')
+for img, ax in zip(combined_xray_images_1, axes):
+    ax.imshow(img, cmap='gray')
+    ax.axis('off')
 ```
 
 **4.** In addition, it can be helpful to show the progress as an animation.