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medical.tar.gz

medical

In this course, you’ll learn the basics of medical image analysis using Python. You will learn to display and interpret X-ray and CT scans. This course uses relevant Python libraries and commands on medical images for format conversion, segmentation, and analyzing metadata. You can successfully complete this course with only a basic understanding of Python and no background in medical imaging.

Medical Image Analysis in Python

## Data and format
In this course, we’ll use open-source data. We’ll use the X-ray dataset for pneumothorax segmentation in the DICOM format. For the NIfTI-1 Data Format, we’ll use the CT dataset for liver cancer segmentation. This course does not require you to download the dataset to your computer. However, if you want to save it to your computer, see the course’s appendix.

## DICOM
To work with DICOM files, we’ll use the `pydicom` library. We import all the libraries necessary for the work. After importing the required libraries, we read the file using the `dcmread()`. Let’s take the file as an example to display the given X-ray image.

As a result, the image data variable is of type `pydicom.dataset.FileDataset` since it contains not only the snapshot but also the **metadata**. To access the image, we need to use the  pixel_array attribute of the **DICOM** image. Render the image using **Matplotlib** and use `plt.cm.bone` as a color scheme to make the image look like an X-ray.
 


The result will look like this:









import pydicom # library for working with DICOM files
from matplotlib import cm # color schemes for visualization
from matplotlib import pyplot as plt # library for visualization
example = 'stage_2_images/ID_01fe90211.dcm'
imagedata= pydicom.dcmread(example)
plt.figure(figsize=(12, 12))
plt.imshow(imagedata.pixel_array, cmap=plt.cm.bone)
plt.show()

**Self-study task:** Remove the `plt.cm.bone` color scheme, or try other color scheme.





## NIfTI-1 
The **NIfTI DFWG (Data Format Working Group)** suggested NIfTI-1 as a short-term approach to enhance the interoperability of functional MRI data analysis software packages.

Let’s look at an example to understand this approach.

* First, we import all the libraries necessary for this work.
* Then, we read the file using `load()`.
* We use `get_fdata()`to get an image.
* We work with the file `volume_pt5/volume-44.nii`. Turn the image $90$ degrees, and look at the shape of the file contents.
* The “depth” here means CT slices. We use `imshow()` to visualize $50$ and $118$ CT slices.
 
> **Note**: We can see the output of the array after turning the image by clicking on the `>` symbol.

Let’s visualize a couple of CT slices ($50$ and $118$ in this case):

import nibabel # library for working with NIfTI-1 Data Format 
import numpy as np # numpy for image manipulation
from matplotlib import cm # color schemes for visualization
from matplotlib import pyplot as plt # library for visualization
filepath = 'volume_pt5/volume-44.nii' 
imagedata=nibabel.load(filepath) 
array = imagedata.get_fdata()
array = np.rot90(np.array(array)) 
print(array.shape)
f = plt.figure(figsize=(12,12))
ax = f.add_subplot(121)
ax2 = f.add_subplot(122) 
ax.imshow(array[...,50].astype(np.float32), cmap=plt.cm.bone) 
ax2.imshow(array[...,118].astype(np.float32), cmap=plt.cm.bone)


**Self-study task:** Visualize various values of CT slices.

# Data and format
In this course, we’ll use open-source data. We’ll use the X-ray dataset for pneumothorax segmentation in the DICOM format. For the NIfTI-1 Data Format, we’ll use the CT dataset for liver cancer segmentation. This course does not require you to download the dataset to your computer. However, if you want to save it to your computer, see the course’s appendix.

# DICOM
To work with DICOM files, we’ll use the `pydicom` library. We import all the libraries necessary for the work. After importing the required libraries, we read the file using the `dcmread()`. Let’s take the file as an example to display the given X-ray image.

As a result, the image data variable is of type `pydicom.dataset.FileDataset` since it contains not only the snapshot but also the **metadata**. To access the image, we need to use the  pixel_array attribute of the **DICOM** image. Render the image using **Matplotlib** and use `plt.cm.bone` as a color scheme to make the image look like an X-ray.
 


The result will look like this:









# NIfTI-1 
The **NIfTI DFWG (Data Format Working Group)** suggested NIfTI-1 as a short-term approach to enhance the interoperability of functional MRI data analysis software packages.

Let’s look at an example to understand this approach.

* First, we import all the libraries necessary for this work.
* Then, we read the file using `load()`.
* We use `get_fdata()`to get an image.
* We work with the file `volume_pt5/volume-44.nii`. Turn the image $90$ degrees, and look at the shape of the file contents.
* The “depth” here means CT slices. We use `imshow()` to visualize $50$ and $118$ CT slices.
 
> **Note**: We can see the output of the array after turning the image by clicking on the `>` symbol.

Let’s visualize a couple of CT slices ($50$ and $118$ in this case):

Learn to display a medical image stored in a file.

__default

Loading and Visualization

Data and format

DICOM

NIfTI-1