RIA (version 1.6.1)

glcm: Creates gray-level co-occurrence matrix of RIA image

Description

Creates gray-level co-occurrence matrix (GLCM) from RIA_image. GLCM assesses the spatial relation of voxels to each other. By default the $modif image will be used to calculate GLCMs. If use_slot is given, then the data present in RIA_image$use_slot will be used for calculations. Results will be saved into the glcm slot. The name of the subslot is determined by the supplied string in save_name, or is automatically generated by RIA.

Usage

glcm(
  RIA_data_in,
  off_right = 1,
  off_down = 0,
  off_z = 0,
  symmetric = TRUE,
  normalize = TRUE,
  use_type = "single",
  use_orig = FALSE,
  use_slot = NULL,
  save_name = NULL,
  verbose_in = TRUE
)

Value

RIA_image containing the GLCM.

Arguments

RIA_data_in

RIA_image.

off_right

integer, indicating the number of voxels to look to the right. Negative values indicate to the left.

off_down

integer, indicating the number of voxels to look down. Negative values indicate up.

off_z

integer, indicating the number of voxels to look in cross plane.

symmetric

logical, indicating whether to create a symmetric glcm by also calculating the glcm in the opposite direction (-1*off_right; -1*off_down; -1*off_z), and add it to the glcm

normalize

logical, indicating whether to change glcm elements to relaive frequencies.

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.

References

Robert M. HARALICK et al. Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics. 1973; SMC-3:610-621. DOI: 10.1109/TSMC.1973.4309314 https://ieeexplore.ieee.org/document/4309314/

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 GLCM 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 <- glcm(RIA_image, use_orig = FALSE, verbose_in = TRUE)

#Use use_slot parameter to set which image to use
RIA_image <- glcm(RIA_image, use_orig = FALSE, use_slot = "discretized$ep_4",
off_right = 2, off_down = -1, off_z = 0)

#Batch calculation of GLCM matrices on all discretized images
RIA_image <- glcm(RIA_image, use_type = "discretized",
off_right = 1, off_down = -1, off_z = 0)
}