read arbitrary medical images in python
Project description
pymedio
Read arbitrary medical images in Python with various backends for scientific computing.
Basically, this package is just a modified version of torchio [1] and dicom-numpy which eagerly loads images (depending on the backend and settings) and returns them as numpy arrays (instead of PyTorch tensors, as is the case in torchio).
There are also various factory functions to load and work with images from memory buffers instead of from disk which is preferable in certain environments, e.g., AWS Lambda.
The main motivation for yet another medical image reader is that I wanted the flexibility of torchio in opening almost any medical image type, without requiring PyTorch and without the automatic casting of images to arrays of 32-bit floating point precision, which is an unnecessary and memory-intensive operation in many scientific computing use cases.
Free software: MIT license
Documentation: https://pymedio.readthedocs.io.
Install
The easiest way to install the package is through the following command:
pip install pymedio
To install from the source directory, clone the repo and run:
python setup.py install
The package only comes with pydicom installed by default; if you want to load non-DICOM images (using SimpleITK or nibabel as a backend), install with:
pip install "pymedio[all]"
Basic Usage
Say you have a directory of DICOM images at the path dicom_dir, then you can open it with:
import medio.dicom as miod
image = miod.DICOMImage.from_path("dicom_dir")This uses pydicom as a backend to open the image à la dicom-numpy.
If you have a NIfTI image at image.nii, and you installed the package with all extras, you can open it with:
import medio.image as mioi
image = mioi.Image.from_path("image.nii")In either case, you can proceed to work with the image data like a normal numpy array, e.g.,
image += 1.0
image *= imageYou can convert an either image class to a torch tensor—if you have it installed—like:
import torch
tensor = torch.as_tensor(image.torch_compatible())References
History
0.1.3 (2021-12-23)
Lazy load DICOM files to reduce peak memory consumption
Make DICOM and base image classes (more) immutable
0.1.2 (2021-12-22)
Make Image classes proper subclasses of ndarray
Add type hints to support normal numpy operations on Images
0.1.1 (2021-12-21)
Avoid version collision
0.1.0 (2021-12-21)
First release on PyPI.
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