RIA (version 1.6.1)

glrlm_stat: GLRLM-based statistics

Description

Calculates GLRLM-based statistics for given GLRLM matrix.

Usage

glrlm_stat(
  RIA_data_in,
  use_type = "single",
  use_orig = FALSE,
  use_slot = "glrlm$es_8_111",
  save_name = NULL,
  verbose_in = TRUE
)

Value

RIA_image containing the statistical information.

Arguments

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". "glrlm" takes all datasets in the RIA_image$glrlm 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. However, GLRLM matrices are usually note present in either slots, therefore giving the slot name using use_slot is advised.

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, ig the desired dataset is in RIA_image$glrlm$ep_4, then use_slot should be glrlm$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 $glrlm to save results to. If left empty, then it will be automatically determined.

verbose_in

logical, indicating whether to print detailed information. Most prints can also be suppressed using the suppressMessages function.

References

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://pubmed.ncbi.nlm.nih.gov/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://pubmed.ncbi.nlm.nih.gov/28346329/

Examples

if (FALSE) {
#Discretize loaded image and then calculate GLRLM statistics
RIA_image <- discretize(RIA_image, bins_in = 8, equal_prob = TRUE)
RIA_image <- glrlm(RIA_image, use_orig = FALSE, use_slot = "discretized$ep_8",
right = TRUE, down = TRUE, forward = FALSE)
RIA_image <- glrlm_stat(RIA_image, use_orig = FALSE, use_slot = "glrlm$ep_8_110")

#Batch calculation of GLRLM-based statistics on all calculated GLRLMs
RIA_image <- glrlm_stat(RIA_image, use_type = "discretized")
}