sp.similarity.from.bin {espadon} | R Documentation |
Volume-based spatial similarity metrics calculated from binary modality 3D volumes.
Description
The sp.similarity.from.bin
function computes volumetric Dice
similarity coefficient, Dice-Jaccard coefficient and Dice surface similarity coefficient.
Usage
sp.similarity.from.bin(
vol.A,
vol.B,
coeff = c("DSC", "DJC", "MDC", "under.MDC", "over.MDC")
)
Arguments
vol.A , vol.B |
"volume" class objects, of |
coeff |
Vector indicating the requested metrics from among
'DSC' (Dice similarity coefficient),'DJC' (Dice-Jaccard coefficient),
and 'MDC' (mean distance to conformity). Equal to |
Value
returns a dataframe containing (if requested):
volumetric Dice similarity coefficient
DSC
defined by : \[DSC = 2 \frac{V_{A} ~\cap~ V_{B}}{V_{A} + V_{B}}\]Dice-Jaccard coefficient
DJC
defined by : \[DJC = \frac{V_{A} ~\cap~ V_{B}}{V_{A} ~\cup~ V_{B}}\]mean distance to conformity
MDC
, over-contouring mean distanceover.MDC
and under-contouring mean distanceunder.MDC
, defined by Jena et al [1]
References
[1] Jena R, et al. (2010). “A novel algorithm for the morphometric assessment of radiotherapy treatment planning volumes.” Br J Radiol., 83(985), 44-51. doi:10.1259/bjr/27674581.
See Also
Examples
# creation of to volume" class objects, of "binary" modality
vol.A <- vol.create (pt000 = c(-25,-25,0), dxyz = c (1 , 1, 1),
n.ijk = c(50, 50, 1), default.value = FALSE,
ref.pseudo = "ref1",
alias = "vol.A", modality = "binary",
description = "")
vol.B <- vol.copy (vol.A,alias = "vol.B")
vol.A$vol3D.data [as.matrix(expand.grid(15:35,20:35,1))] <- TRUE
vol.A$max.pixel <- TRUE
vol.B$vol3D.data [as.matrix(expand.grid(16:36,18:37,1))] <- TRUE
vol.B$max.pixel <- TRUE
display.plane (vol.A, vol.B, interpolate = FALSE,
main = "vol.A & vol.B @ z = 0 mm")
sp.similarity.from.bin (vol.A, vol.B)