pydicom

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.

Installation

Using pip:

pip install pydicom

Using conda:

conda install -c conda-forge pydicom

For more information, including installation instructions for the development version, see the installation guide.

Documentation

The pydicom user guide, tutorials, examples and API reference documentation is available for both the current release and the development version on GitHub Pages.

Pixel Data

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)

Compressed Pixel Data

JPEG, JPEG-LS and JPEG 2000

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.

Currently only JPEG-LS (with pyjpegls) is supported for compressing data.

RLE

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.

Examples

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()

Contributing

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.