--- a
+++ b/R/radiomics_partition.R
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+#' Radiomic Calculation on Partitioned Lung
+#'
+#' Calculate radiomic features on the partitioned 3D lung
+#'
+#' @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 featuresFirst First level radiomic features to calculate
+#' @param featuresSpatial Spatial radiomic features to calculate
+#' @param partition Matrix of x, y, and z coordinates for each partition from partition_lung. If null, partition_lung is called.
+#' @param kernel_size (If partition is null) Size of the kernel, in voxel units of width, depth, and height. Must be c(3,3,3) or greater. Default: c(30,30,30)
+#' @param kernel_stride (If partition is null) Stride (or spacing) between kernels, in voxel units, for width, depth, and height. If kernel_stride = kernel_size, the partitions are non-overlapping. If stride = c(1,1,1), then each voxel is returned.
+#' @param threshold Number of non-missing voxels needed to calculate radiomic features in each partition.
+#' @param tidy Logical. If true, outputs a tidy dataframe with results. If false, outputs nested loop.
+#'
+#' @return Values from selected features for both left and right lungs
+#' @importFrom ANTsR maskImage
+#' @export
+radiomics_partition <- function(img,
+                                mask,
+                                sides = c("right", "left"),
+                                featuresFirst = c('mean', 'sd', 'skew', 'kurtosis', 'min', 'q1', 'median', 'q3', 'max','energy', 'rms', 'uniformity', 'entropy'),
+                                featuresSpatial = c('mi', 'gc', 'fd'),
+                                partition = NULL,
+                                kernel_size = c(30, 30, 30),
+                                kernel_stride = c(30, 30, 30),
+                                threshold = 1000,
+                                tidy = TRUE) {
+
+  # Get partition, if necessary
+  if(is.null(partition)){
+    partition = partition_lung(img,
+                               kernel_size = kernel_size,
+                               kernel_stride = kernel_stride,
+                               centroid = TRUE)
+  }
+
+
+  # Calculate radiomic features on partitions within each mask value
+  featuresMask <- lapply(sides, function(side){
+
+    if(side == "right"){mv = 1}
+    if(side == "left"){mv = 2}
+
+    # Put image in array format and remove non-mask values
+    img2 <- as.array(img)
+    mask2 <- as.array(mask)
+    mask2 <- mask2 == mv
+    img2[mask2 != 1] <- NA
+
+    # Calculate n each partition
+    features <- lapply(1:dim(partition)[1], function(i){
+
+      # Grab partition
+      x <- img2[partition$x1[i]:partition$xend[i],
+                partition$y1[i]:partition$yend[i],
+                partition$z1[i]:partition$zend[i]]
+
+      # Find dimension of partition and number of non-null pixels
+      dim_p <- dim(x)
+      if(is.null(dim_p)){dim_p <- c(0,0,0)}
+      npixels <- length(x[!is.na(x)])
+
+
+      # Only calculate radiomic features if partition fits criteria
+      if(dim_p[1] > 2 & dim_p[2] > 2 & dim_p[3] > 2 & npixels >= threshold){
+
+        # Calculate features
+        if(length(featuresFirst)>0){
+          features1 <- radiomics_first(x, featuresFirst)
+        }else(features1 <- NULL)
+        if(length(featuresSpatial)>0){
+          features2 <- radiomics_spatial(x, featuresSpatial)
+        }else(features2 <- NULL)
+
+
+        # Put features together and only keep specified features
+        features <- c(features1, features2)
+        features <- features[c(featuresFirst, featuresSpatial)]
+        features <- c(npixels = npixels, features)
+
+      }else(features <- NULL)
+
+      return(features)
+    })
+
+    # Name partitions and remove NULL partitions
+    names(features) <- paste0('partition', 1:dim(partition)[1])
+    features[sapply(features, is.null)] <- NULL
+    return(features)
+  })
+  names(featuresMask) <- sides
+
+
+  if(tidy == TRUE){
+    # Make a nice little data frame to output
+    test2 = NULL
+    for(i in 1:length(featuresMask)){
+
+      # Reduce list to data frame
+      test <- do.call('rbind', featuresMask[[i]])
+      test <- cbind.data.frame(lung = names(featuresMask)[i],
+                               partition = names(featuresMask[[i]]),
+                               test)
+
+      # Reformatting
+      test$partition <- gsub("partition", "", test$partition)
+      test <- as.data.frame(sapply(test, as.numeric))
+
+      # Add in partition centroids
+      partition2 <- partition[partition$partition %in% test$partition, 8:10]
+      test <- cbind(partition2, test)
+      test2 <- rbind(test2, test)
+    }
+    featuresMask <- test2
+    rm(test,test2,partition2)
+    gc()
+    rownames(featuresMask) <- c()
+  }
+
+  return(featuresMask)
+}
+