glrlm_all.RdCreates 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.
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 $glcm to save results to. If left empty, then it will be automatically determined by RIA. |
| verbose_in | logical indicating whether to print detailed information.
Most prints can also be suppressed using the |
RIA_image containing the GLRLMs.
Márton KOLOSSVÁRY et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign Circulation: Cardiovascular Imaging (2017). DOI: 10.1161/circimaging.117.006843 https://www.ncbi.nlm.nih.gov/pubmed/29233836
Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2018). DOI: 10.1097/RTI.0000000000000268 https://www.ncbi.nlm.nih.gov/pubmed/28346329
if (FALSE) {
#Discretize loaded image and then calculate GLRLM matrix of RIA_image$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)
}