mritc (version 0.3-2)

mritc: MRI Tissue Classification Using Various Methods

Description

Conduct the MRI tissue classification using different methods including: the normal mixture model (NMM) fitted by the Expectation-Maximization (EM) algorithm; the hidden Markov normal mixture model (HMNMM) fitted by the Iterated Conditional Mode (ICM) algorithm, the Hidden Markov Random Field EM (HMRFEM) algorithm, or the Bayesian method (MCMC); the partial volume HMNMM fitted by the modified EM (PVHMRFEM) algorithm ; the higher resolution HMNMM fitted by the Bayesian method (MCMCsub).

Usage

mritc.em(y, prop, mu, sigma, err, maxit, verbose)
   mritc.icm(y, neighbors, blocks, spatialMat, beta, mu, sigma,
             err, maxit, verbose)
   mritc.hmrfem(y, neighbors, blocks, spatialMat, beta, mu, sigma,
                err, maxit, verbose)
   mritc.pvhmrfem(y, neighbors, blocks, spatialMat, beta, mu, sigma,
              err, maxit, verbose)
   mritc.bayes(y, neighbors, blocks, sub, subvox, spatialMat, beta,
               mu, sigma, niter, verbose)
   mritc(intarr, mask, method)

Arguments

y

a vector of intensity values of voxels.

prop

a vector of initial estimate of the proportions of different components of a normal mixture model. It can be obtained using the function initOtsu.

mu

a vector of initial estimate of the means of different components of a normal mixture model. It can be obtained using the function initOtsu.

sigma

a vector of initial estimates of the standard deviations of different components of a normal mixture model. It can be obtained using the function initOtsu.

err

relative maximum error(s) used to decide when to stop the iteration. It could be a vector corresponding to the relative maximum errors of the means, standard deviations (for mritc.em, <

maxit

maximum number of iterations to perform. The default is 200 for mritc.em, 20 for mritc.icm, mr

verbose

logical. If TRUE, then indicate the level of output as the algorithm runs.

neighbors

a matrix of neighbors of voxels. One row per voxel. It can be obtained using the function makeMRIspatial.

blocks

split voxels into different blocks to use the checker-board idea. It can be obtained using the function makeMRIspatial.

spatialMat

a matrix defining the spatial relationship in a Potts model. The default value is diag(1,3) for three components models for mritc.icm,

beta

the parameter 'inverse temperature' of the Potts model. The default value is 0.4 for mritc.icm, 0.5 for mritc.hmrfem, 0.6 for

sub

logical; if TRUE, use the higher resolution model; otherwise, use the whole voxel method.

subvox

for mritc.bayes, the match up tabel of voxels and their corresponding subvoxels for the higher resolution model. It can be obtained using the function

niter

the number of iterations for mritc.bayes. The default value is 100, which seems to be adequate in many cases.

intarr

a three dimensional array of an MR image.

mask

a mask of the MR image. Voxels with value 1 are inside the brain and value 0 are outside. Focus on voxels within the brain.

method

a string giving the method for MRI tissue classification. It must be one of "EM", "ICM", "HMRFEM", "MCMC", "PVHMRFEM", or "MCMCsub", corresponding to using the NMM fitted by the EM algorithm; the HMNMM fitted by the ICM algorithm, the HMRFEM algor

Value

• For mritc, it generates an object of class "mritc" which is a list containing the following components:
• prob
  a matrix, one row per voxel and each column corresponding to the probabilities of being allocated to each component of a normal mixture model.
• mu
  a vector of estimated means of the normal mixture model.
• sigma
  a vector of estimated standard deviations of the normal mixture model.
• method
  the method used for computation.
• mask
  mask of a brain. Voxels inside it are classified.
• Generic functions print.mritc, summary.mritc, and plot.mritc are provided.

  For others, only prob, mu, and sigma are generated.

Details

The function mritc integrates functions mritc.em, mritc.icm, mritc.hmrfem, mritc.pvhmrfem, and mritc.bayes. It provides a uniform platform with easier usage. The user just need to specify the input MR image, the mask of the image, and the method used. The other parameters are specified automatically as follows. The parameters for the initial estimates of the proportions, means, and standard deviations of the normal mixture model are obtained using the function initOtsu. As to the parameters related to the Potts model, the six neighbor structure is used and then the neighbors, blocks, and subvox are obtained using the function makeMRIspatial. The other parameters are taken as the default values for each method. The process is reported during iterations.

References

Julian Besag (1886) On the statistical analysis of dirty pictures (with discussion) Journal of the Royal Statistical Society. Series B (Methodological) vol. 48 259-302 Yongyue Zhang, Michael Brady, and Stephen Smith (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm IEEE Transactions on Medical Imaging vol. 20 45-57

Meritxell Bach Cuadra, Leila Cammoun, Torsten Butz, Olivier Cuisenaire, and Jean-Philippe Thiran (2005) Comparison and Validation of Tissue modelization and statistical classification methods in T1-weighted {MR} brain images IEEE Transactions on Medical Imaging, vol.24 1548-1565 Dai Feng (2008) Bayesian Hidden Markov Normal Mixture Models with Application to MRI Tissue Classification Ph. D. Dissertation, The University of Iowa

Examples

#Example 1
  T1 <- readMRI(system.file("extdata/t1.rawb.gz", package="mritc"),
                c(91,109,91), format="rawb.gz")
  mask <- readMRI(system.file("extdata/mask.rawb.gz", package="mritc"),
                  c(91,109,91), format="rawb.gz")
  y <- T1[mask==1]
  initial <- initOtsu(y, 2)
  prop <- initial$prop
  mu <- initial$mu
  sigma <- initial$sigma
  tc.em <- mritc.em(y, prop, mu, sigma, verbose=TRUE)
 
  mrispatial <- makeMRIspatial(mask, nnei=6, sub=FALSE)
  tc.icm <- mritc.icm(y, mrispatial$neighbors, mrispatial$blocks,
                      mu=mu, sigma=sigma, verbose=TRUE)
  tc.hmrfem <- mritc.hmrfem(y, mrispatial$neighbors, mrispatial$blocks,
                            mu=mu, sigma=sigma, verbose=TRUE)
  tc.pvhmrfem <- mritc.pvhmrfem(y, mrispatial$neighbors, mrispatial$blocks,
                                mu=mu, sigma=sigma, verbose=TRUE)
  tc.mcmc <- mritc.bayes(y, mrispatial$neighbors, mrispatial$blocks,
                         mrispatial$sub, mrispatial$subvox,
                         mu=mu, sigma=sigma, verbose=TRUE)
  mrispatial <- makeMRIspatial(mask, nnei=6, sub=TRUE)
  tc.mcmcsub <- mritc.bayes(y, mrispatial$neighbors, mrispatial$blocks,
                         mrispatial$sub, mrispatial$subvox,
                         mu=mu, sigma=sigma, verbose=TRUE)
  #Example 2
  T1 <- readMRI(system.file("extdata/t1.rawb.gz", package="mritc"),
                c(91,109,91), format="rawb.gz")
  mask <-readMRI(system.file("extdata/mask.rawb.gz", package="mritc"),
                 c(91,109,91), format="rawb.gz")
  tc.icm <- mritc(T1, mask, method="ICM")