Package RIA details

Radiomics Image Analysis Toolbox for Medial Images

Radiomics image analysis toolbox for 2D and 3D radiological images. RIA supports DICOM, NIfTI and nrrd 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.

Maintainer: Marton Kolossvary < marton.kolossvary at gmail.com >

 
From within R, enter citation('RIA')


To cite package 'RIA' in publications use:

Mrton Kolossvry, Jlia Kardy, Blint Szilveszter, Pieter Kitslaar,
Udo Hoffmann, Bla Merkely, Pl Maurovich-Horvat (2017). Radiomic
Features Are Superior to Conventional Quantitative Computed
Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring
Sign Circulation: Cardiovascular Imaging,
DOI:10.1161/circimaging.117.006843

Mrton Kolossvry, Mikls Kellermayer, Bla Merkely, Pl
Maurovich-Horvat (2018). Cardiac Computed Tomography Radiomics: A
Comprehensive Review on Radiomic Techniques. Journal of Thoracic
Imaging, DOI:10.1097/RTI.0000000000000268

To see these entries in BibTeX format, use 'print(,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.

 

If you have any problems with this package you can open a new issue or check the already existing ones here.

 
 
To install this package, start R and enter:

source("https://neuroconductor.org/neurocLite.R")

# Default Install
neuro_install('RIA')

# from GitHub
neuro_install('RIA', release = "stable", release_repo = "github")
neuro_install('RIA', release = "current", release_repo = "github")

More detailed installation instructions can be found here.

 

Initially submitted on September 16 2019 2:33PM
Last updated on September 16 2019 2:33PM
Package type standard
Source GitHub https://github.com/cran/RIA GitHub
Neuroconductor GitHub https://github.com/neuroconductor/RIA GitHub
DependsR (3.3.0)
Importsoro.dicom (0.5.0), oro.nifti (0.9.1), nat (1.8.11)
Suggestsknitr, rmarkdown
Reverse Suggestslungct