Understanding and processing MRI data can be tricky and confusing. In this blog, I will provide a basic introduction on how to load and process MRI data using the most important Python libraries.
MRI data mainly consists of three pieces of information.
-> Header (metadata)
-> Affine (Represents the affine transformation)
-> Image data (N-D Array)
Most of the medical imaging data will be in the Nifti (.nii) and Dicom (.dcm) formats. We will be discussing Nifti format data, dicom format will be discussed in further posts.
Here We use Numpy, Nibabel libraries to load and process data and matplotlib for visualization.
If you don’t have these libraries installed, you can install using pip package manager in your python environment entering the following commands:
pip install numpy
pip install niabel
pip install matplotlib
import nibabel as nib import numpy as np
data = nib.load("path to Nifti format data") #(.nii or .nii.gz)
Here data is a data object containing header, affine, and image data with all required attributes for further processing.
image_data = data.get_fdata()
get_fdata() function returns a floating-point NumPy matrix containing image pixel data
(256, 256, 128)
output shape represents that image_data is a 3d volume, z-axis(128) represents the number of slices in MRI data
To check the affine coordinates
This will output an array relating array coordinates from the image data array to coordinates in some RAS+ world coordinate system
RAS (Right, Anterior, Superior)
For a detailed understanding of coordinate systems of neuroimaging go through the following link:
To check the header info of the data object
A detailed understanding of metadata and affine transformations will be discussed in a next post.
Visualizing MRI slices
import matplotlib.pyplot as plt plt.imshow(image_data[:,:,64], cmap="gray")
You can change the integer value in the above code snippet with in a range of (0-127) to visualize different slices.
<matplotlib.image.AxesImage at 0x1f6cb300148>
For a detailed understanding on Indexing using NumPy go through the following link
In further posts, I will be discussing how to prepare MRI data to apply machine learning algorithms, noise removal techniques, and different pre-processing and post-processing techniques.