glrlm.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. By default the $modif image will be used to calculate GLRLMs. If use_slot is given, then the data present in RIA_image$use_slot will be used for calculations. Results will be saved into the glrlm slot. The name of the subslot is determined by the supplied string in save_name, or is automatically generated by RIA. off_right, off_down and off_z logicals are used to indicate the direction of the runs.
glrlm(RIA_data_in, off_right = 1, off_down = 0, off_z = 0,
use_type = "single", use_orig = FALSE, use_slot = NULL,
save_name = NULL, verbose_in = TRUE)| RIA_data_in | RIA_image. |
|---|---|
| off_right | integer, positive values indicate to look to the right, negative values indicate to look to the left, while 0 indicates no offset in the X plane. |
| off_down | integer, positive values indicate to look to the right, negative values indicate to look to the left, while 0 indicates no offset in the Y plane. |
| off_z | integer, positive values indicate to look to the right, negative values indicate to look to the left, while 0 indicates no offset in the Z plane. |
| 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 $glcm to save results to. If left empty, then it will be automatically determined based on the last entry of RIA_image$log$events. |
| verbose_in | logical indicating whether to print detailed information.
Most prints can also be suppressed using the |
RIA_image containing the GLRLM.
Mary M. Galloway et al. Texture analysis using gray level run lengths. Computer Graphics and Image Processing. 1975; 4:172-179. DOI: 10.1016/S0146-664X(75)80008-6 https://www.sciencedirect.com/science/article/pii/S0146664X75800086
Márton KOLOSSVÁRY et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign Circulation: Cardiovascular Imaging (2017). DOI: 10.1161/circimaging.117.006843 https://www.ncbi.nlm.nih.gov/pubmed/29233836
Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2018). DOI: 10.1097/RTI.0000000000000268 https://www.ncbi.nlm.nih.gov/pubmed/28346329
if (FALSE) {
#Discretize loaded image and then calculate GLRLM matrix of RIA_image$modif
RIA_image <- discretize(RIA_image, bins_in = c(4, 8), equal_prob = TRUE,
use_orig = TRUE, write_orig = FALSE)
RIA_image <- glrlm(RIA_image, use_orig = FALSE, verbose_in = TRUE)
#Use use_slot parameter to set which image to use
RIA_image <- glrlm(RIA_image, use_orig = FALSE, use_slot = "discretized$ep_4",
off_right = 1, off_down = 1, off_z = 0)
#Batch calculation of GLRLM matrices on all discretized images
RIA_image <- glrlm(RIA_image, use_type = "discretized",
off_right = 1, off_down = 1, off_z = 0)
}