glrlm_all: Creates gray-level run length matrix of all possible...
In neuroconductor/RIA: Radiomics Image Analysis Toolbox for Medial Images

Description Usage Arguments Value References Examples

Creates gray-level run length matrix (GLRLM) from RIA_image. GLRLM assesses the spatial relation of voxels to each other by investigating how many times same value voxels occur next to each other in a given direction. While the glrlm function calculates the GLRLM in one given direction, the glrlm_all function simultaneously calculates all GLRLMs in all possible directions. For 3D datasets, this means GLCMs will be calculated for all 13 different directions. In case of 2D datasets, only 4 are returned by default. By default the use_type is set to discretize, therefore GLRLMs will be calculated for all discretized images in all directions. Also single data processing is supported, then by default the image in the $modif slot will be used. If use_slot is given, then the data present in RIA_image$use_slot will be used for calculations. Results will be saved into the glrlm slot. The name of the subslot is automatically generated by RIA.

1 2	glrlm_all(RIA_data_in, use_type = "discretized", use_orig = FALSE, use_slot = NULL, save_name = NULL, verbose_in = TRUE)

`RIA_data_in`	RIA_image.
`use_type`	string, can be "single" which runs the function on a single image, which is determined using "use_orig" or "use_slot". "discretized" takes all datasets in the RIA_image$discretized slot and runs the analysis on them.
`use_orig`	logical, indicating to use image present in RIA_data$orig. If FALSE, the modified image will be used stored in RIA_data$modif.
`use_slot`	string, name of slot where data wished to be used is. Use if the desired image is not in the data$orig or data$modif slot of the RIA_image. For example, if the desired dataset is in RIA_image$discretized$ep_4, then use_slot should be discretized$ep_4. The results are automatically saved. If the results are not saved to the desired slot, then please use save_name parameter.
`save_name`	string, indicating the name of subslot of $glcm to save results to. If left empty, then it will be automatically determined by RIA.
`verbose_in`	logical indicating whether to print detailed information. Most prints can also be suppressed using the `suppressMessages` function.

RIA_image containing the GLRLMs.

Márton KOLOSSVÁRY et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign Circulation: Cardiovascular Imaging (2017). DOI: 10.1161/circimaging.117.006843 https://www.ncbi.nlm.nih.gov/pubmed/29233836

Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2018). DOI: 10.1097/RTI.0000000000000268 https://www.ncbi.nlm.nih.gov/pubmed/28346329

## Not run: 
#Discretize loaded image and then calculate GLRLM matrix of RIA_image$modif
RIA_image <- discretize(RIA_image, bins_in = c(4, 8), equal_prob = TRUE,
use_orig = TRUE, write_orig = FALSE)
RIA_image <- glrlm_all(RIA_image, use_type = "single")

#Use use_slot parameter to set which image to use
RIA_image <- glrlm_all(RIA_image, use_type = "single",
use_orig = FALSE, use_slot = "discretized$ep_4")

#Batch calculation of GLCM matrices on all disretized images
RIA_image <- glrlm_all(RIA_image)

## End(Not run)