segment | R Documentation |

Segmentation of 3D images using EM algorithms

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 )

`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 |

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

## 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)

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