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`

, `nrrd`

and `npy`

(numpy arrays
save in python) 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:

```
#Load the data by providing the location of the DICOM, NIfTI or nrrd file(s)
<- load_dicom(filename = "C:/Image/")
DICOM
#Calculate first-order, GLCM, GLRLM and geometry based statistics
<- radiomics_all(DICOM, equal_prob = "both")
DICOM
#Save output to csv
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.