segment: Segmentation of 3D images using EM algorithms
In bioimagetools: Tools for Microscopy Imaging

segment

R Documentation

Segmentation of 3D images using EM algorithms

Description

Segmentation of 3D images using EM algorithms

Usage

segment(
  img,
  nclust,
  beta,
  z.scale = 0,
  method = "cem",
  varfixed = TRUE,
  maxit = 30,
  mask = array(TRUE, dim(img)),
  priormu = rep(NA, nclust),
  priormusd = rep(NULL, nclust),
  min.eps = 10^{
     -7
 },
  inforce.nclust = FALSE,
  start = NULL,
  silent = FALSE
)

Arguments

`img`	is a 3d array representing an image.
`nclust`	is the number of clusters/classes to be segmented.
`beta`	is a matrix of size nclust x nclust, representing the prior weight of classes neighboring each other.
`z.scale`	ratio of voxel dimension in x/y direction and z direction. Will be multiplied on beta for neighboring voxel in z direction.
`method`	only "cem" classification EM algorithm implemented.
`varfixed`	is a logical variable. If TRUE, the variance is equal in each class.
`maxit`	is the maximum number of iterations.
`mask`	is a logical array, representing the voxels to be used in the segmentation.
`priormu`	is a vector with mean of the normal prior of the expected values of all classes. Default is NA, which represents no prior assumption.
`priormusd`	is a vector with standard deviations of the normal prior of the expected values of all classes.
`min.eps`	stop criterion. Minimal change in sum of squared estimate of mean in order to stop.
`inforce.nclust`	if TRUE enforces number of clusters to be nclust. Otherwise classes might be removed during algorithm.
`start`	not used
`silent`	if TRUE, function remains silent during running time

Value

A list with "class": 3d array of class per voxel; "mu" estimated means; "sigma": estimated standard deviations.

Examples

## Not run: 
original<-array(1,c(300,300,50))
for (i in 1:5)original[(i*60)-(0:20),,]<-original[(i*60)-(0:20),,]+1
for (i in 1:10)original[,(i*30)-(0:15),]<-original[,(i*30)-(0:15),]+1
original[,,26:50]<-4-aperm(original[,,26:50],c(2,1,3))

img<-array(rnorm(300*300*50,original,.2),c(300,300,50))
img<-img-min(img)
img<-img/max(img)

try1<-segment(img,3,beta=0.5,z.scale=.3)
print(sum(try1$class!=original)/prod(dim(original)))

beta<-matrix(rep(-.5,9),nrow=3)
beta<-beta+1.5*diag(3)
try2<-segment(img,3,beta,z.scale=.3)
print(sum(try2$class!=original)/prod(dim(original)))

par(mfrow=c(2,2))
img(original)
img(img)
img(try1$class)
img(try2$class)

## End(Not run)

bioimagetools documentation built on May 28, 2022, 5:06 p.m.