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)
```

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")
```

)

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"
```

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))`

:

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))
```

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