Welcome to pyradiomics documentation!¶
This is an open-source python package for the extraction of Radiomics features from medical imaging. With this package we aim to establish a reference standard for Radiomic Analysis, and provide a tested and maintained open-source platform for easy and reproducible Radiomic Feature extraction. By doing so, we hope to increase awareness of radiomic capabilities and expand the community. The platform supports both the feature extraction in 2D and 3D.
If you publish any work which uses this package, please cite the following publication: van Griethuysen, J. J. M., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R. G. H., Fillon-Robin, J. C., Pieper, S., Aerts, H. J. W. L. (2017). Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Research, 77(21), e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339
This work was supported in part by the US National Cancer Institute grant 5U24CA194354, QUANTITATIVE RADIOMICS SYSTEM DECODING THE TUMOR PHENOTYPE.
Not intended for clinical use.
Table of Contents¶
- Customizing the Extraction
- Pipeline Modules
- Radiomic Features
- Excluded Radiomic Features
- Contributing to pyradiomics
- Release Notes
Currently supports the following feature classes:
First Order Statistics
Gray Level Cooccurence Matrix(GLCM)
Gray Level Run Length Matrix(GLRLM)
Gray Level Size Zone Matrix(GLSZM)
Neigbouring Gray Tone Difference Matrix(NGTDM)
Gray Level Dependence Matrix(GLDM)
On average, Pyradiomics extracts \(\approx 1500\) features per image, which consist of the 16 shape descriptors and features extracted from original and derived images (LoG with 5 sigma levels, 1 level of Wavelet decomposistions yielding 8 derived images and images derived using Square, Square Root, Logarithm and Exponential filters).
Detailed description on feature classes and individual features is provided in section Radiomic Features.
Aside from the feature classes, there are also some built-in optional filters:
Laplacian of Gaussian(LoG, based on SimpleITK functionality)
Wavelet(using the PyWavelets package)
Local Binary Pattern (2D)
Local Binary Pattern (3D)
For more information, see also Image Processing and Filters.
Supporting reproducible extraction¶
Aside from calculating features, the pyradiomics package includes additional information in the output. This information contains information on used image and mask, as well as applied settings and filters, thereby enabling fully reproducible feature extraction. For more information, see General Info Module.
3rd-party packages used in pyradiomics¶
- SimpleITK (Image loading and preprocessing)
- numpy (Feature calculation)
- PyWavelets (Wavelet filter)
- pykwalify (Enabling yaml parameters file checking)
- six (Python 3 Compatibility)
See also the requirements file.
PyRadiomics is OS independent and compatible with both Python 2.7 and Python >=3.4. As of version 2.0, PyRadiomics releases are also made available on PyPi. To install PyRadiomics, ensure you have python installed and run:
python -m pip install pyradiomics
For more detailed installation instructions and building from source, see Installation section.
1Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 2Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 3Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands, 4GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands, 5Kitware, 6Isomics
We are happy to help you with any questions. Please contact us on the pyradiomics email list.
We’d welcome your contributions to PyRadiomics. Please read the contributing guidelines on how to contribute to PyRadiomics. Information on adding / customizing feature classes and filters can be found in the Developers section.