README.md

In joelcarlson/radiomics: 'Radiomic' Image Processing Toolbox

Radiomics: Texture Analysis Matrices

** Not Currently Maintained **

This project is not currently being maintained. While I will do my best to help in a timely fashion, you should not expect a prompt response.

The radiomics package is a set of tools for computing texture matrices and features from images.

The release version of this package (April 2016, v0.1.2) is available from CRAN using:

install.packages("radiomics")

Or you can install the development version of the package using:

devtools::install_github("joelcarlson/radiomics")

library(radiomics)

Texture Matrices

In the package are functions for calculating four different types of matrices and associated feature sets used to quantify the texture of an image.

These matrices are the:

• Grey Level Co-occurrence Matrix
• Grey Level Run Length Matrix
• Grey Level Size Zone Matrix
• Multiple Grey Level Size Zone Matrix

Detailed usage directions for calculating features and matrices can be found in the package vignette (use browseVignettes(package = "radiomics"))

Using the Package

Building Texture Matrices

Texture matrices can be created from 2D images by using the abbreviated and lowercase matrix name as a function call:

tumor <- radiomics::tumor #2D MRI slice of a brain tumor
glcm(tumor)
glrlm(tumor)
glszm(tumor)
mglszm(tumor)

A matrix with the class of the texture matrix type is returned, as shown here using glcm(tumor, n_grey=4)

#> An object of class "glcm"
#>              1          2           3            4
#> 1 0.1617021277 0.03356974 0.001891253 0.0004728132
#> 2 0.0335697400 0.38345154 0.010638298 0.0014184397
#> 3 0.0018912530 0.01063830 0.301654846 0.0184397163
#> 4 0.0004728132 0.00141844 0.018439716 0.0203309693

class(glcm(tumor, n_grey=4))[1]
#> [1] "glcm"

Visualizing Texture Matrices

Each matrix type has an associated image function for visualization of the results:

image(glcm(tumor))
image(glrlm(tumor))
image(glszm(tumor))
image(mglszm(tumor))

The image functions make use of the viridis scale, as shown here using image(glcm(tumor, n_grey=64)):

Calculating Features

Each matrix type has an associated calc_features function, which returns an object of class data.frame with a single observation for each calculated feature. First order features can also be calculated on 2D matrices.

calc_features(tumor)
calc_features(glcm(tumor))
calc_features(glrlm(tumor))
calc_features(glszm(tumor))
calc_features(mglszm(tumor))

joelcarlson/radiomics documentation built on May 19, 2019, 2:59 p.m.