Welcome to pyradiomics documentation!

This is an open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks.

Image loading and preprocessing (e.g. resampling and cropping) are first done using SimpleITK. Then, loaded data are converted into numpy arrays for further calculation using feature classes outlined below.

Feature Classes

Currently supports the following feature classes:

• First Order Statistics
• Shape-based
• Gray Level Cooccurence Matrix (GLCM)
• Gray Level Run Length Matrix (GLRLM)
• Gray Level Size Zone Matrix (GLSZM)

Filter Classes

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)
• Square
• Square Root
• Logarithm
• Exponential

Supporting reproducible extraction

Aside from calculating features, the pyradiomics package includes provenance 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.

Citation

If you publish any work which uses this package, please cite the following publication:

Joost J.M. van Griethuysen et al, “Computational Radiomics System to Decode the Radiographic Phenotype”; Submitted

3rd-party packages used in pyradiomics

• SimpleITK
• numpy
• PyWavelets (Wavelet filter)
• pykwalify (Enabling yaml parameters file checking)
• tqdm (Progressbar)
• sphinx (Generating documentation)
• sphinx_rtd_theme (Template for documentation)
• nose-parameterized (Testing)

Installation

• Clone the repository
  • git clone git://github.com/Radiomics/pyradiomics
• Install on your system, with prerequisites:
  • cd pyradiomics
  • sudo python -m pip install -r requirements.txt
  • sudo python setup.py install
• For more detailed installation instructions see Installation Details

Table of Contents

• Installation
  • Get the code
  • Installation on your system
• Usage
  • Example
  • Command Line Use
  • Interactive Use
  • PyRadiomics in 3D Slicer
  • Using feature classes directly
• radiomics package
  • Submodules
  • radiomics.base module
  • radiomics.featureextractor module
  • radiomics.generalinfo module
  • radiomics.firstorder module
  • radiomics.glcm module
  • radiomics.glszm module
  • radiomics.glrlm module
  • radiomics.shape module
  • radiomics.imageoperations module
  • Module contents

Pyradiomics Indices and Tables

• Module Index
• Index
• Search Page

License

This package is covered by the 3D Slicer License.

This work was supported in part by the US National Cancer Institute grant 5U24CA194354, QUANTITATIVE RADIOMICS SYSTEM DECODING THE TUMOR PHENOTYPE.

Developers

• Joost van Griethuysen ^1,3,4
• Andriy Fedorov ²
• Nicole Aucoin ²
• Jean-Christophe Fillion-Robin⁵
• Ahmed Hosny ¹
• Steve Pieper ⁶
• Hugo Aerts (PI)^1,2

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

General references

• HJWL Aerts, ER Velazquez, RTH Leijenaar, et al., “Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach”, vol. 5, Nat Communication, 2014. Available here. Specifically, the formulation of the individual feature calculation is covered in this supplement.
• Zwanenburg A, Leger S, Vallières M, Löck S., “Image biomarker standardisation initiative - feature definitions”, arXiv:161207003. 2016. Available here.