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Added alt text for the images in the x-ray image processing tutorial #101

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rossbar merged 1 commit into from
Aug 14, 2021
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Added alt text for the images in the x-ray image processing tutorial #101

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14 changes: 7 additions & 7 deletions content/tutorial-x-ray-image-processing.md
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Expand Up @@ -22,7 +22,7 @@ on certain parts, and visually compare them using the
[Laplacian-Gaussian](https://en.wikipedia.org/wiki/Laplace_distribution),
[Sobel](https://en.wikipedia.org/wiki/Sobel_operator), and
[Canny](https://en.wikipedia.org/wiki/Canny_edge_detector) filters for edge
detection.
detection.

X-ray image analysis can be part of your data analysis and
[machine learning workflow](https://www.sciencedirect.com/science/article/pii/S235291481930214X)
Expand Down Expand Up @@ -50,7 +50,7 @@ tutorial's repository under `tutorial-x-ray-image-processing/`, since
ChestX-ray8 contains gigabytes of data and you may find it challenging to
download it in batches.

![Images showing different filters applied over a chest x-ray image](_static/tutorial-x-ray-image-processing.png)
![A series of 9 x-ray images of the same region of a patient's chest is shown with different types of image processing filters applied to each image. Each x-ray shows different types of biological detail.](_static/tutorial-x-ray-image-processing.png)

+++

Expand Down Expand Up @@ -104,7 +104,7 @@ each notebook cell.
+++

Let's begin with a simple example using just one X-ray image from the
ChestX-ray8 dataset.
ChestX-ray8 dataset.

The file — `00000011_001.png` — has been downloaded for you and saved in the
`/tutorial-x-ray-image-processing` folder.
Expand Down Expand Up @@ -234,10 +234,10 @@ notebook:

```{code-cell} ipython3
GIF_PATH = os.path.join(DIR, 'xray_image.gif')
imageio.mimwrite(GIF_PATH, combined_xray_images_1, format= '.gif', fps = 1)
imageio.mimwrite(GIF_PATH, combined_xray_images_1, format= '.gif', fps = 1)
```
Which gives us:
![health_progress](tutorial-x-ray-image-processing/xray_image.gif)
![An animated gif repeatedly cycles through a series of 8 x-rays, showing the same viewpoint of the patient's chest at different points in time. The patient's bones and internal organs can be visually compared from frame to frame.](tutorial-x-ray-image-processing/xray_image.gif)
## Edge detection using the Laplacian-Gaussian, Gaussian gradient, Sobel, and Canny filters

+++
Expand Down Expand Up @@ -341,7 +341,7 @@ produce a gradient magnitude.
[Pythagorean theorem](https://en.wikipedia.org/wiki/Pythagorean_theorem) and
NumPy's [`np.hypot()`](https://numpy.org/doc/stable/reference/generated/numpy.hypot.html)
to obtain the magnitude. Finally, normalize the rescaled image for the pixel
values to be between 0 and 255.
values to be between 0 and 255.

[Image normalization](https://en.wikipedia.org/wiki/Normalization_%28image_processing%29)
follows the `output_channel = 255.0 * (input_channel - min_value) / (max_value - min_value)`
Expand Down Expand Up @@ -488,7 +488,7 @@ plt.show()
```

As the pixel intensity distribution suggests, there are many low (between around
0 and 20) and very high (between around 200 and 240) pixel values.
0 and 20) and very high (between around 200 and 240) pixel values.

**3.** You can create different conditional masks with NumPy's `np.where()` —
for example, let's have only those values of the image with the pixels exceeding
Expand Down