compute_all_metrics

Calculate set of the following Haralick texture features; contrast, entropy, energy,
autocorrelation, correlation, cluster prominence, cluster shade, homogeneity,
inverse difference, max probability, and sum of squares.

Generalizes application of gray-level co-occurrence matrix
(GLCM) metrics to objects outside of images. The current focus is to
apply GLCM metrics to the study of biological networks and fitness
landscapes that are used in studying evolutionary medicine and
biology, particularly the evolution of cancer resistance. The package was
developed as part of the author's publication in Physics in Medicine and Biology
Barker-Clarke et al. (2023) <doi:10.1088/1361-6560/ace305>.
A general reference to learn more about mathematical oncology can be found at
Rockne et al. (2019) <doi:10.1088/1478-3975/ab1a09>.

Rowan Barker-Clarke

gtexture

Generalized Application of Co-Occurrence Matrices and Haralick
Texture

Raoul Wadhwa

Davis Weaver

Jacob Scott

compute_all_metrics function

<dl><dt>x</dt>
<dd>Input glcm matrix</dd></dl>

Arguments

Convenience function to compute all haralick texture features — compute_all_metrics

<dl>

<dt>x</dt>
<dd>Input glcm matrix</dd>

</dl>

compute_all_metrics: Convenience function to compute all haralick texture features

Description

Usage

Value

Arguments