glcm_stat.Rd
Calculates GLCM-based statistics for given GLCM matrix.
glcm_stat(RIA_data_in, use_type = "single", use_orig = FALSE,
use_slot = "glcm$es_8_111", save_name = NULL, verbose_in = TRUE)
RIA_data_in | RIA_image. |
---|---|
use_type | string, can be "single" which runs the function on a single image, which is determined using "use_orig" or "use_slot". "glcm" takes all datasets in the RIA_image$glcm 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, GLCM matrices are usually not 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$glcm$ep_4_111, then use_slot should be glcm$ep_4_111. The results are automatically saved. If the results are not saved to the desired slot, then please use save_name parameter. If the string contains "-" characters use "`" before the last slot name, for example: glcm$`ep_4_-1-1-1` |
save_name | string, indicating the name of subslot of $glcm to save results to. If left empty, then it will be automatically determined. |
verbose_in | logical, indicating whether to print detailed information.
Most prints can also be suppressed using the |
RIA_image containing the statistical information.
Márton KOLOSSVÁRY et al. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign Circulation: Cardiovascular Imaging (2017). DOI: 10.1161/circimaging.117.006843 https://www.ncbi.nlm.nih.gov/pubmed/29233836
Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2018). DOI: 10.1097/RTI.0000000000000268 https://www.ncbi.nlm.nih.gov/pubmed/28346329
if (FALSE) {
#Discretize loaded image and then calculate GLCM statistics
RIA_image <- discretize(RIA_image, bins_in = 8, equal_prob = TRUE)
RIA_image <- glcm(RIA_image, use_orig = FALSE, use_slot = "discretized$ep_8",
off_right = 0, off_down = 1, off_z = 0)
RIA_image <- glcm_stat(RIA_image, use_orig = FALSE, use_slot = "glcm$ep_8_010")
#Batch calculation of GLCM-based statistics on all calculated GLCMs
RIA_image <- glcm_stat(RIA_image, use_type = "discretized")
}