Python bindings for a fast RLE decoder, with a focus on use as a plugin for pylibjpeg

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  • License: GNU General Public License v3 (GPLv3) (MIT)
  • Author: scaramallion
  • Tags dicom, pydicom, python, medicalimaging, radiotherapy, oncology, imaging, radiology, nuclearmedicine, rle, pylibjpeg, rust
  • Requires: Python >=3.6
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Project description

pylibjpeg-rle

A fast DICOM (PackBits) RLE plugin for pylibjpeg, written in Rust with a Python 3.6+ wrapper.

Linux, MacOS and Windows are all supported.

Installation

Installing the current release

pip install pylibjpeg-rle

Installing the development version

Make sure Python, Git and Rust are installed. For Windows, you also need to install Microsoft's C++ Build Tools.

git clone https://github.com/pydicom/pylibjpeg-rle
cd pylibjpeg-rle
python -m setup.py develop

Supported Transfer Syntaxes

UID Description Decoding Encoding
1.2.840.10008.1.2.5 RLE Lossless Yes No

Benchmarks

Decoding

Time per 1000 decodes, pydicom's NumPy RLE handler vs. pylibjpeg-rle

Dataset Pixels Bytes NumPy pylibjpeg-rle
OBXXXX1A_rle.dcm 480,000 480,000 4.89 s 0.79 s
OBXXXX1A_rle_2frame.dcm 960,000 960,000 9.89 s 1.65 s
SC_rgb_rle.dcm 10,000 30,000 0.20 s 0.15 s
SC_rgb_rle_2frame.dcm 20,000 60,000 0.32 s 0.18 s
MR_small_RLE.dcm 4,096 8,192 0.35 s 0.13 s
emri_small_RLE.dcm 40,960 81,920 1.13 s 0.28 s
SC_rgb_rle_16bit.dcm 10,000 60,000 0.33 s 0.17 s
SC_rgb_rle_16bit_2frame.dcm 20,000 120,000 0.56 s 0.21 s
rtdose_rle_1frame.dcm 100 400 0.12 s 0.13 s
rtdose_rle.dcm 1,500 6,000 0.53 s 0.26 s
SC_rgb_rle_32bit.dcm 10,000 120,000 0.56 s 0.19 s
SC_rgb_rle_32bit_2frame.dcm 20,000 240,000 1.03 s 0.28 s

Usage

Decoding

With pylibjpeg

Because pydicom defaults to the NumPy RLE decoder, you must specify the use of pylibjpeg when decompressing:

from pydicom import dcmread
from pydicom.data import get_testdata_file

ds = dcmread(get_testdata_file("OBXXXX1A_rle.dcm"))
ds.decompress("pylibjpeg")
arr = ds.pixel_array

Standalone with pydicom

Alternatively you can use the included functions to decode a given dataset:

from rle import pixel_array, generate_frames

# Return the entire Pixel Data as an ndarray
arr = pixel_array(ds)

# Generator function that only processes 1 frame at a time,
# may help reduce memory usage when dealing with large Pixel Data
for arr in generate_frames(ds):
    print(arr.shape)

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  • License: GNU General Public License v3 (GPLv3) (MIT)
  • Author: scaramallion
  • Tags dicom, pydicom, python, medicalimaging, radiotherapy, oncology, imaging, radiology, nuclearmedicine, rle, pylibjpeg, rust
  • Requires: Python >=3.6
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2.0.0

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