rootSizeDir {coreCT}

Convert a directory of raw DICOM images to root particle counts and surface areas

Description

Calculates the number of root/rhizome particles and surface areas, for different size classes. This approach directly replicates Earl Davey's manual classification approach. This method is deprecated as of coreCT version 1.3.0.

Usage

rootSizeDir(directory = file.choose(), diameter.classes = c(1, 2, 5, 10, 20),
class.names = diameter.classes,
airHU = -850.3233,
airSD = 77.6953,
waterHU = 63.912,
waterSD = 14.1728,
pixel.minimum = 1)

Arguments

directory

a character string that can be a matrix of DICOM images or the address of an individual DICOM file in a folder of DICOM images. The default action is <code>file.choose()</code>; a browser menu appears so the user can select the the desired directory by identifying a single DICOM file in the folder of images.

diameter.classes

an integer vector of diameter cut points. Units are mm (zero is added in automatically).

class.names

not used presently

airHU

mean value for air-filled calibration rod (all rod arguments are in Hounsfield Units)

airSD

standard deviation for air-filled calibration rod

waterHU

mean value for water-filled calibration rod

waterSD

standard deviation for water-filled calibration rod

pixel.minimum

minimum number of pixels needed for a clump to be identified as a root

Details

Calculates the number of root/rhizome particles and surface areas, for different size classes. Unlike rootSize, rootSizeDir accepts a folder of raw values and makes the conversion to Hounsfield Units using the metadata associated with the DICOM images.

Value

value rootSize returns a dataframe with one row per CT slice. Values returned are the number, volume (cm3), and surface area (cm2) of particles in each size class with an upper bound defined in diameter.classes.

See Also

rootSizeDir is a wrapper for rootSize. rootSizeDir operates similarly.

Examples

rootChars <- rootSizeDir("core_426", diameter.classes = c(2.5, 10))

## Not run: 
# plot using "ggplot" package after transforming with "reshape2" package
area.long <- reshape2::melt(rootChars, id.vars = c("depth"), 
   measure.vars = grep("Area", names(rootChars)))
ggplot2::ggplot(data = area.long, ggplot2::aes(y = -depth, x = value, 
   color = variable)) + ggplot2::geom_point() + ggplot2::theme_classic() + 
   ggplot2::xlab("root external surface area per slice (cm2)")

## End(Not run)