coreCT (version 1.3.3)

rootSize: Convert a matrix of semi-processed DICOM images to root particle counts, volumes, and surface areas

Description

Calculates the number of root/rhizome particles, volumes, 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

rootSize(mat.list, pixelA, diameter.classes = c(1, 2, 2.5, 10),
class.names = diameter.classes,
thickness = 0.625,
airHU = -850.3233,
airSD = 77.6953,
waterHU = 63.912,
waterSD = 14.1728,
pixel.minimum = 4)

Arguments

mat.list

list of DICOM images for a sediment core (values in Hounsfield Units)

pixelA

pixel area (mm2)

diameter.classes

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

class.names

not used presently

thickness

CT image thickness (mm)

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

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.

Details

Calculates the number of root/rhizome particles, volumes, and surface areas, for different size classes. This function requires that values be Hounsfield Units (i.e., data must be semi-processed from the raw DICOM imagery).

See Also

conv

Examples

# NOT RUN {
ct.slope <- unique(extractHeader(core_426$hdr, "RescaleSlope"))
ct.int   <- unique(extractHeader(core_426$hdr, "RescaleIntercept")) 
# convert raw units to Hounsfield units
HU_426 <- lapply(core_426$img, function(x) x*ct.slope + ct.int)

rootChars <- rootSize(HU_426, pixelA = 0.0596,
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)")
# }
# NOT RUN {
# }