|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Pydicom||1,631||347||250||9 days ago||27||March 29, 2022||54||other||Python|
|Read, modify and write DICOM files with python code|
|Highdicom||121||3||24 days ago||36||November 09, 2022||23||mit||Python|
|High-level DICOM abstractions for the Python programming language|
|Dicomweb Client||92||3 months ago||8||mit||Python|
|Python client for DICOMweb RESTful services|
|Dicom_rt_and_images_to_mask||60||a month ago||54||December 02, 2021||gpl-3.0||Python|
|Various utilities created to help with the interpretation of DICOM images and DICOM RT Structures to NumPy masks and Simple-ITK handles/Nifti images. Some necessary steps for deep learning|
|Dicombrowser||23||9 days ago||gpl-3.0||Python|
|Lightweight portable Dicom browser application|
|Embedded Pydicom React Viewer||16||2 years ago||other|
|Medical DICOM file P10 Viewer/Chrome Extension + 🐍 Python 🐍 Code In Browser (-Pyodide-> WebAssembly) + Pydicom parser + TypeScript React App (CRA) + Python FastAPI Server Deployment|
|Numpyviewer||9||a year ago||gpl-2.0||C++|
|Viewer for hyperspectral Numpy cubes and (2d/3d/4d)-tensors. (GPLv2)|
|Pylibjpeg Openjpeg||8||10||4 months ago||6||February 03, 2022||5||other||Python|
|A J2K and JP2 plugin for pylibjpeg|
|Medimage||6||3 years ago||lgpl-3.0||Python|
|Represent medical images as numpy array. Supported: .mhd (R/W),.xdr (R/W), dicom (R). Pure Python.|
|TUTORIAL -> Django bootstrap 4 DICOM online viewer using python background|
pydicom is a pure Python package for working with DICOM files. It lets you read, modify and write DICOM data in an easy "pythonic" way. As a pure Python package, pydicom can run anywhere Python runs without any other requirements, although if you're working with Pixel Data then we recommend you also install NumPy.
Note that pydicom is a general-purpose DICOM framework concerned with reading and writing DICOM datasets. In order to keep the project manageable, it does not handle the specifics of individual SOP classes or other aspects of DICOM. Other libraries both inside and outside the pydicom organization are based on pydicom and provide support for other aspects of DICOM, and for more specific applications.
Examples are pynetdicom, which is a Python library for DICOM networking, and deid, which supports the anonymization of DICOM files.
pip install pydicom
conda install -c conda-forge pydicom
For more information, including installation instructions for the development version, see the installation guide.
The pydicom user guide, tutorials, examples and API reference documentation is available for both the current release and the development version on GitHub Pages.
Compressed and uncompressed Pixel Data is always available to be read, changed and written as bytes:
>>> from pydicom import dcmread >>> from pydicom.data import get_testdata_file >>> path = get_testdata_file("CT_small.dcm") >>> ds = dcmread(path) >>> type(ds.PixelData) <class 'bytes'> >>> len(ds.PixelData) 32768 >>> ds.PixelData[:2] b'\xaf\x00'
If NumPy is installed, Pixel Data can be converted to an ndarray using the Dataset.pixel_array property:
>>> arr = ds.pixel_array >>> arr.shape (128, 128) >>> arr array([[175, 180, 166, ..., 203, 207, 216], [186, 183, 157, ..., 181, 190, 239], [184, 180, 171, ..., 152, 164, 235], ..., [906, 910, 923, ..., 922, 929, 927], [914, 954, 938, ..., 942, 925, 905], [959, 955, 916, ..., 911, 904, 909]], dtype=int16)
Converting JPEG compressed Pixel Data to an
ndarray requires installing one or more additional Python libraries. For information on which libraries are required, see the pixel data handler documentation.
Compressing data into one of the JPEG formats is not currently supported.
Encoding and decoding RLE Pixel Data only requires NumPy, however it can be quite slow. You may want to consider installing one or more additional Python libraries to speed up the process.
More examples are available in the documentation.
Change a patient's ID
from pydicom import dcmread ds = dcmread("/path/to/file.dcm") # Edit the (0010,0020) 'Patient ID' element ds.PatientID = "12345678" ds.save_as("/path/to/file_updated.dcm")
Display the Pixel Data
With NumPy and matplotlib
import matplotlib.pyplot as plt from pydicom import dcmread from pydicom.data import get_testdata_file # The path to a pydicom test dataset path = get_testdata_file("CT_small.dcm") ds = dcmread(path) # `arr` is a numpy.ndarray arr = ds.pixel_array plt.imshow(arr, cmap="gray") plt.show()
We are all volunteers working on pydicom in our free time. As our resources are limited, we very much value your contributions, be it bug fixes, new core features, or documentation improvements. For more information, please read our contribution guide.
If you have examples or extensions of pydicom that don't belong with the core software, but that you deem useful to others, you can add them to our contribution repository: contrib-pydicom.