--- a
+++ b/R/radiomics_slice.R
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+#' Radiomic Calculation on CT Slices
+#'
+#' Calculate radiomic features on the each 2D slice of the whole 3D lung, right and left lungs separately
+#'
+#' @param img CT scan in ANTs image file format
+#' @param mask Mask of CT scan in ANTs image file format
+#' @param sides Choose to calculate radiomic features on the right and/or left lungs. Note: Right lung = 1, left lung = 2, non-lung = 0
+#' @param plane One of: axial, coronal, sagittal
+#' @param featuresFirst First level radiomic features to calculate
+#' @param featuresSpatial Spatial radiomic features to calculate
+#' @param tidy Logical. If true, outputs a tidy dataframe with results. If false, outputs nested loop.
+#' @param reduce Logical. If true, reduces the dimensions of the scan based on extent of mask using reduce_scan.
+#'
+#' @return Radiomic values from every slice in both the right and left lungs
+#' @export
+#'
+radiomics_slice <- function(img,
+                           mask,
+                           sides = c("right", "left"),
+                           plane = 'axial',
+                           featuresFirst = c('mean', 'sd', 'skew', 'kurtosis', 'min', 'q1', 'median', 'q3', 'max','energy', 'rms', 'uniformity', 'entropy'),
+                           featuresSpatial = c('mi', 'gc', 'fd'),
+                           tidy = TRUE,
+                           reduce = TRUE){
+
+
+  featuresMask <- lapply(sides, function(side){
+
+    if(side == "right"){mv = 1}
+    if(side == "left"){mv = 2}
+
+    mask2 <- mask == mv
+
+    # Reduce scan (optional)
+    if(reduce == TRUE){
+      red <- reduce_scan(img, mask2)
+      img2 <- red$img
+      mask2 <- red$mask
+      rm(red)
+      gc()
+    }else{img2 <- img}
+
+    # Put image in array format and remove non-mask values
+    img2 <- as.array(img2)
+    mask2 <- as.array(mask2)
+    img2[mask2 != 1] <- NA
+
+
+    # Which plane?
+    if(plane == 'axial'){p = 3}
+    if(plane == 'coronal'){p = 2}
+    if(plane == 'sagittal'){p = 1}
+
+
+    # Calculate features
+    ndim <- dim(img2)[p]
+    features <- apply(img2, p, function(x){
+      npixels <- length(x[!is.na(x)])
+      if(length(featuresFirst)>0){
+        features1 <- radiomics_first(x, featuresFirst)
+      }else(features1 <- NULL)
+      if(length(featuresSpatial)>0){
+        features2 <- radiomics_spatial(x, featuresSpatial)
+      }else(features2 <- NULL)
+      features <- c(features1, features2)
+      features <- features[c(featuresFirst, featuresSpatial)]
+      features <- c(npixels = npixels, features)
+      return(features)
+    })
+    names(features) <- paste0('slic_num_', 1:ndim)
+
+    return(features)
+  })
+  names(featuresMask) <- sides
+
+
+  if(tidy == TRUE){
+    # Make a nice little data frame to output
+    test2 = NULL
+    for(i in 1:length(featuresMask)){
+      test <- do.call('rbind', featuresMask[[i]])
+      test <- cbind.data.frame(lung = names(featuresMask)[i],
+                               slice_number = names(featuresMask[[i]]),
+                               test)
+      test$slice_number <- gsub("slic_num_", "", test$slice_number)
+      test <- as.data.frame(sapply(test, as.numeric))
+      nslic <- dim(test)[1]
+      test$slice_percent <- test$slice_number/nslic * 100
+      test2 <- rbind(test2, test)
+    }
+    featuresMask <- test2
+    rownames(featuresMask) <- c()
+  }
+
+  return(featuresMask)
+}
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