radiomics-features

Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics

Cancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk

Image processing tools for radiomics analysis

Python Implementation of the CoLlAGe radiomics descriptor. CoLlAGe captures subtle anisotropic differences in disease pathologies by measuring entropy of co-occurrences of voxel-level gradient orientations on imaging computed within a local neighborhood.

Clinically-Interpretable Radiomics [MICCAI'22, CMPB'21]

Predict survival time from PET scans

Reference MATLAB and Python implementations of the RADISTAT algorithm

Code to Implement the Smooth Euler Characteristic Transform (SECT)

Classification of spondylodiscitidis vs metastasis in the spine using multiple approaches in the RStudio

Python implementation of topology descriptors which capture subtle sharpness and curvature differences along the surface of diseased pathologies on imaging.

Classification of spondylodiscitidis vs metastasis in the spine using Neural Networks

Calculate 43 texture features of a 2D or 3D image

SQLite4Radiomics is a Conquest DICOM & pyradiomics integration software project