dpmixsim (version 0.0-9)

postkcluster: Segmentation with a fixed number of clusters

Description

postkcluster re-clusters the data with a user-specified number of components, and displays the segmented image.

Usage

postkcluster(mask, cx, clk=4, plot=TRUE)

Arguments

mask

masked full-sized image data prepared by premask

cx

data segmentation prepared by postdataseg

clk

desired fixed number of components, including the background component, to use in the data segmentation; default clk=4: gray matter (GM), white matter (WM), CSF, and background

plot

logical variable; enables suspension of output images (default = TRUE)

Details

Partitioning clustering around medoids (PAM) is applied to the classes simulated from dpmixsim as a post-processing step. This procedure may be applied to merge clusters, and reduce the number of clusters to the specified value clk. postkcluster computes a clara object using cluster (see Struyf et.al.), a list representing a clustering of the data into clk clusters.

References

Adelino Ferreira da Silva, A Dirichlet process mixture model for brain MRI tissue classification, Medical Image Analysis 11 (2007) 169-182.

Adelino Ferreira da Silva, Bayesian mixture models of variable dimension for image segmentation, Comput. Methods Programs Biomed. 94 (2009) 1-14.

Anja Struyf, Mia Hubert & Peter J. Rousseeuw (1996): Clustering in an Object-Oriented Environment. Journal of Statistical Software, 1. http://www.stat.ucla.edu/journals/jss/

See Also

dpmixsim, readsliceimg, premask, postdpmixciz, postdataseg, postimgcomps

Examples

# NOT RUN {
## see Examples in `dpmixsim'.
# }