sp.similarity.from.mesh: Distance-based spatial similarity metrics calculated from the mesh.

Description

The sp.similarity.from.mesh function computes Hausdorff distances and surface Dice similarity coefficient.

Usage

sp.similarity.from.mesh(
  mesh1,
  mesh2,
  hausdorff.coeff = c("HD.max", "HD.mean"),
  hausdorff.quantile = c(0.5, 0.95),
  surface.tol = seq(0, 10, 1)
)

Value

Returns a list containing (if requested):

Hausdorff : dataframe including the maximum, mean and quantiles
smetrics : dataframe with the columns:
- tol : the requested tolerances
- sDSC : the surface Dice similarity coefficients,defined by Nikolov et al [1]
- sAPL : the surface Added Path Length in mm^2ascii, introduced (in pixels) by Vaassen et al [2]

Arguments

mesh1, mesh2: espadon mesh class objects
hausdorff.coeff: Vector indicating the requested Hausdorff distance metrics from among 'HD.max','HD.mean'. Equal to NULL if not requested. NULL, it will be added.
hausdorff.quantile: numeric vector of probabilities with values between 0 and 1, representing the quantiles of the unsigned distances between mesh1 and mesh2. Equal to NULL if not requested.
surface.tol: numeric vector representing the maximum margins of deviation which may be tolerated without penalty. Equal to NULL if not requested.

References

[1] Nikolov2018DeepLTespadon

[2] Vaassen2020Evalespadon

Examples

Run this code

library (Rvcg)
# espadon mesh of two spheres of radius R1=10 and R2=11, separated by dR = 3
sph <- vcgSphere ()
mesh1 <- obj.create ("mesh")
mesh1$nb.faces <- ncol (sph$it)
mesh1$mesh <- sph
mesh2 <-  mesh1

R1 <- 10
R2 <- 11
dR <- 3
mesh1$mesh$vb[1:3,] <- R1 * mesh1$mesh$normals[1:3,] + mesh1$mesh$vb[1:3,]
mesh2$mesh$vb[1:3,] <- R2 * mesh2$mesh$normals[1:3,] + mesh2$mesh$vb[1:3,] +
                 matrix (c (dR, 0, 0), ncol = ncol (mesh2$mesh$vb), nrow = 3)
             
sp.similarity.from.mesh (mesh1 , mesh2, 
                         hausdorff.quantile = seq (0, 1, 0.05),
                         surface.tol = seq (0, dR + abs(R2-R1), 0.5))