Radiomics image analysis toolbox for 2D and 3D radiological images. RIA supports DICOM, NIfTI, nrrd and npy (numpy array) file formats. RIA calculates first-order, gray level co-occurrence matrix, gray level run length matrix and geometry-based statistics. Almost all calculations are done using vectorized formulas to optimize run speeds. Calculation of several thousands of parameters only takes minutes on a single core of a conventional PC.

Radiomics Image Analysis (RIA) package was developed to facilitate radiomic analysis of medical images. `RIA`

can calculate hundreds of different statistics on both 2D and 3D images. `RIA`

supports analysis of `DICOM`

, `NIfTI`

and `nrrd`

images. Almost all calculations are vectorized and therefore are super-efficient. The package is developed by Márton Kolossváry a medical doctor not an engineer, therefore all functionalities of the software package are developed in a way that can be learnt by non-professionals. `RIA`

is constantly updated with new functionalities and wrap-around functions to make the calculation of radiomic metrics even simpler.

RIA allows users to take control of each and every aspect of radiomic analysis using specific functions. However, for most users 3 lines of simple code: one loading the data and one calculating the statistics, and one exporting the results is enough:

DICOM <- load_dicom(filename = "C:/Image/")#Calculate first-order, GLCM, GLRLM and geometry based statisticsDICOM <- radiomics_all(DICOM, equal_prob = FALSE, bins_in = c(8,16,32), distance = c(1:2), fo_discretized = FALSE, geometry_discretized = TRUE)save_RIA(DICOM, save_to = "C:/Test/", save_name = "My_first_radiomics", group_name = "Case")

These three simple lines of code result in thousands of radiomic parameters calculated for the given image! For a more detailed introduction to RIA please read the vignette. If you wish to better understand Radiomics I would suggest reading "Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques" and "Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign" which describes the calculation and each statistic in detail in the supplementary files.