glcm_stat: Calculates GLCM-based statistics
In neuroconductor/RIA: Radiomics Image Analysis Toolbox for Medial Images

Description Usage Arguments Value References Examples

Calculates GLCM-based statistics for given GLCM matrix.

1 2	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 `suppressMessages` function.

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

## Not run: 
#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")

## End(Not run)