Feature extraction algorithms for prostate created at MRCTurku http:https://mrc.utu.fi/.

If you use this work in publication, please cite:

Merisaari, H, Taimen, P, Shiradkar, R, et al. Repeatability of radiomics and machine learning for DWI: Short‐term repeatability study of 112 patients with prostate cancer. Magn Reson Med. 2019; 00: 1– 17. https://doi.org/10.1002/mrm.28058

For further information about the project, please see related ISMRM 2019 abstract:

H Merisaari, R Shiradkar, J Toivonen, A Hiremath, M Khorrami, IM Perez, T Pahikkala, P Taimen, J Verho, PJ Boström, H Aronen, A Madabhushi, I Jambor, Repeatability of radiomics features for prostate cancer diffusion weighted imaging obtained using b-values up to 2000 s/mm2, 27th Annual Meeting & Exhibition ISMRM, May 11-16 2019, Montréal, QC, Canada, #7461

Installation instructions:

• scipy image processing tools:

install -c anaconda scipy 

• pyzernikemoment texture features:

pip install pyzernikemoment

• numba (optional for GPU speed-up):

conda install numba

• skimage image processing tools:

conda install skimage

• cv2 iamge processing tools:

pip install opencv

For Pyradiomics wrapper:

• pyradiomics:

pip install pyradiomics

• SimpleITK:

conda install -c simpleitk simpleitk

MRCRadiomics package:

1. Download and extract the repository
2. Install with setup.py

python setup.py install

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.

(c) Harri Merisaari 2018-2022