Package 'mritc' reference manual

Title:	MRI Tissue Classification
Description:	Implements various methods for tissue classification in magnetic resonance (MR) images of the brain, including normal mixture models and hidden Markov normal mixture models, as outlined in Feng & Tierney (2011) <doi:10.18637/jss.v044.i07>. These methods allow a structural MR image to be classified into gray matter, white matter and cerebrospinal fluid tissue types.
Authors:	Dai Feng [aut], Luke Tierney [aut], Jon Clayden [cre, aut]
Maintainer:	Jon Clayden <[email protected]>
License:	GPL
Version:	0.5-3
Built:	2024-11-19 06:36:34 UTC
Source:	CRAN

MRI Tissue Classification Package

Description

Use various methods to do MRI tissue classification.

Introduction

This package provides tools for MRI tissue classification using normal mixture models and hidden Markov normal mixture models (with the partial volume effect and intensity non-uniformity addressed) fitted by various methods.

Magnetic resonance imaging (MRI) is used to identify the major tissues within a subject's brain. Classification is usually based on a single image providing one measurement for each volume element, or voxel, in a discretization of the brain. A simple model for MRI tissue classification views each voxel as homogeneous, belonging entirely to one of the three major tissue types (cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM)); the intensity of voxels are thus normally distributed with means and variances depending on the tissue types of their voxels. The tissue types are not known and need to be identified from the image. The assumption that all tissue types are independent leads to a simple normal mixture model with parameters estimated by the EM algorithm and tissue types assigned using the Bayes classifier.

Since nearby voxels tend to be of the same tissue type, a Markov random field model (a model from the Potts model family is used in this case) can be used to capture the spatial similarity of voxels by assigning homogeneity relationship among tissue types of neighboring voxels. Again, given the tissue types, the intensity of voxels are independently and normally distributed with means and variances depending on their tissue types. Furthermore, the Markov random field model defined on finite space is referred to as the hidden Markov model. Therefore the model combine the normal mixture part and the Potts model part is called the hidden Markov normal mixture model. This model can be fitted by the Iterated Conditional Mode algorithm, the Hidden Markov Random Field EM algorithm, or a Markov chain Monte Carlo approach.

A more realistic model than the one just described would take into account the fact that the volume elements are not homogeneous; while some may contain only one tissue type, others on the interface will contain two or possibly three different tissue types. This phenomenon is called the partial volume (PV) effect. One approach to address the PV effect is to introduce intermediate classes. Usually this is done by introducing two more classes: the combination of the CSF and the GM and the combination of the GM and the WM. Voxels containing WM and CSF are very rare and are ignored. This helps reduce confounding in estimation and a number of studies have used this approach. Among these methods, the Gaussian partial volume hidden Markov random field models fitted by the modified EM algorithm appears to be more competitive in performance. A new approach to this problem is to construct a higher resolution image in which each voxel is divided into eight subvoxels. For each voxel the measured value is the sum of the unobserved measurements for the subvoxels. The subvoxels are in turn assumed to be homogeneous and follow the simpler model described above.

Intensity non-uniformity is an artifact that the signal intensity varies smoothly across an image. It is caused by combination and interaction of effects from the device, pulse sequence, and object. A commonly used approach to tackle it is to assume the the measured signal is equal to true signal multiplied by bias field associated with the intensity non-uniformity plus some noise. The bias field needs to be spatially smoothly varying and is modeled as either jointly normally distributed, or a linear combination of smooth spline or polynomial basis functions. Instead, we propose using a locally smoothed prior on the bias field.

A Bayesian hierarchical model aiming at modeling the partial volume effect and intensity non-uniformity simultaneously was proposed. Instead of splitting the task into different steps, the framework harmoniously integrates several sub-models addressing different issues in the MRI classification, through specification of the likelihood function and prior distributions. This approach could provide more accurate tissue classification and also allow more effective estimation of the proportion of each voxel that belongs to each of the major tissue types.

Besides brain image segmentation, the methods provided in this package can be used for classification of other spatial data as well.

Usage

The function readMRI and writeMRI are I/O functions for MRI data. Right now, the "Analyze", "NIfTI", and raw byte (unsigned with 1 byte per element in the byte stream) gzip formats are supported.

For each MR image, there has to be a corresponding array, mask, with values 1 and 0. Voxels with value 1 are inside the brain and 0 are outside. Tissue classification is conducted on voxels inside the brain.

The functions mritc.em, mritc.icm, mritc.hmrfem, and mritc.bayes are used to conduct the MRI tissue classification using the normal mixture model fitted by the EM algorithm, the hidden Markov normal mixture model at the voxel level fitted by the Iterated Conditional Mode algorithm, the Hidden Markov Random Field EM algorithm, or the Bayesian method (with or without the PV or bias field correction). The function mritc.pvhmrfem is for classification using Gaussian partial volume hidden Markov random field models fitted by the modified EM algorithm. Different components of the normal mixture model correspond to different tissue types. The number of components is flexible, say using five components model to address the PV effect by mritc.em, mritc.icm, mritc.hmrfem, or mritc.bayes.

In order to use the previous functions, the parameters of the normal mixture model and the Potts model have to be specified. Some parameters can be obtained using the functions initOtsu and makeMRIspatial. There are default values for other parameters.

The function mritc integrates all methods together, provides a uniform platform with easier usage, and generates an object of class "mritc" , for which generic functions print.mritc, summary.mritc, and plot.mritc are provided.

Computation Issues

To improve the speed, the table lookup method was used in various places; vectorized computation was used to take advantage of conditional independence. Some computations are performed by C code, and the OpenMP is used to parallelize key loops in the C code. Sparse matrix multiplication is adopted as well.

Estimate the Parameters of a Normal Mixture Model Using the EM Algorithm

Description

Use the EM Algorithm to estimate the parameters of a normal mixture model.

Usage

   emnormmix(y, prop, mu, sigma, err, maxit, verbose)
emnormmix(y, prop, mu, sigma, err, maxit, verbose)

Arguments

`y`	vector of observations.
`prop`	vector of initial estimate of the proportions of different components of a normal mixture model.
`mu`	vector of initial estimate of the means of different components of a normal mixture model.
`sigma`	vector of initial estimate of the standard deviations of different components of a normal mixture model.
`err`	relative maximum error(s) used to decide when to stop the iteration. It could be a vector of length three corresponding to the relative maximum errors of the means, standard deviations, and proportions of all components of a normal mixture model. When it is a scalar, all have the same relative maximum error.
`maxit`	maximum number of iterations to perform.
`verbose`	logical. If `TRUE`, then indicate the level of output as the algorithm runs after every 10 iterations.

Details

It is tailor-made for the case when observations are from a finite set (MRI data for example). The table lookup method is used to speed up the computation.

Value

`prop`	a vector of estimated proportions of different components of a normal mixture model.
`mu`	a vector of estimated means of different components of a normal mixture model.
`sigma`	a vector of estimated standard deviations of different components of a normal mixture model.

Examples

  prop <- c(0.3, 0.3, 0.4)
  mu <- c(-10, 0, 10)
  sigma <- rep(1, 3)
  y<- floor(rnormmix(n=100000, prop, mu, sigma)[,1])
  initial <- initOtsu(y, 2)
  emnormmix(y=y, prop=initial$prop, mu=initial$mu, sigma=initial$sigma,
            err=1e-7, maxit=100, verbose=TRUE)
prop <- c(0.3, 0.3, 0.4)
  mu <- c(-10, 0, 10)
  sigma <- rep(1, 3)
  y<- floor(rnormmix(n=100000, prop, mu, sigma)[,1])
  initial <- initOtsu(y, 2)
  emnormmix(y=y, prop=initial$prop, mu=initial$mu, sigma=initial$sigma,
            err=1e-7, maxit=100, verbose=TRUE)

Get the Initial Estimate of the Parameters of a Normal Mixture Model

Description

Obtain initial estimation of proportions, means, and standard deviations of different components (tissue types for MRI) based on threshold values generated by Otsu's method implemented by a fast algorithm, or proportion of different components.

Usage

   initOtsu(y, m)
   initProp(y, prop)
initOtsu(y, m)
   initProp(y, prop)

Arguments

`y`	a vector of intensity values of an image.
`m`	number of classes (tissue types for MRI) minus 1.
`prop`	the initial estimate of proportion of different components.

Details

The exhaustive search part of the function for Otsu's algorithm is adapted from combn. For initProp, the threshold values are quantiles based on the initial estimate of proportion of different components.

Value

`prop`	a vector of initial estimate of the proportions of different components (tissue types for MRI).
`mu`	a vector of initial estimate of the means of different components (tissue types for MRI).
`sigma`	a vector of initial estimates of the standard deviations of different components (tissue types for MRI).

Note

For initOtsu, it supports any number of m. However, for MRI data, it can be slow if m is bigger than 3 even with the fast algorithm implemented, since the Otsu's algorithm uses an exhaustive search. But it should be fine with m equal to 2, which corresponds to the typical case in MRI classification with three major tissue types CSF, GM, and WM.

References

Nobuyuki Otsu (1979). A threshold selection method from gray-level histograms IEEE Transactions on Systems, Man and Cybernetics vol. 9, 62-66

Ping-Sung Liao, Tse-Sheng Chen and Pau-Choo Chung (2001) A Fast Algorithm for Multilevel Thresholding Journal of Information Science and Engineering vol. 17, 713-727

Examples

  #Example 1
  prop <- c(.3, .4, .3)
  mu <- c(40,  90, 130)
  sigma <- c(4, 8, 4)
  y <- floor(rnormmix(n=100000, prop, mu, sigma)[,1])
  initOtsu(y, 2)
  initProp(y, prop)

  #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")
  initOtsu(T1[mask==1], 2)
  initProp(T1[mask==1], c(0.17, 0.48, 0.35))
#Example 1
  prop <- c(.3, .4, .3)
  mu <- c(40,  90, 130)
  sigma <- c(4, 8, 4)
  y <- floor(rnormmix(n=100000, prop, mu, sigma)[,1])
  initOtsu(y, 2)
  initProp(y, prop)

  #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")
  initOtsu(T1[mask==1], 2)
  initProp(T1[mask==1], c(0.17, 0.48, 0.35))

Obtain Spatial Features of a Mask of an MR Image

Description

Obtain various spatial features of an MR image, which are used in tissue classification.

Usage

   makeMRIspatial(mask, nnei, sub, bias)
makeMRIspatial(mask, nnei, sub, bias)

Arguments

`mask`	three dimensional array. The voxels with value 1 are inside the mask; with value 0 are outside. We just focus on voxels inside the mask.
`nnei`	the number of neighbors. Right now only 6, 18, and 26 neighbors are supported. For a 3D image, besides defining 6 neighbors in the x, y, and z directions, one can add 12 diagonal neighbors in the x-y, x-z, and y-z planes, and another 8 on the 3D diagonals. This leads to a six neighbor structure, an eighteen neighbor structure, and a twenty-six neighbor structure.
`sub`	logical; if `TRUE`, a new `mask` which splits each voxel into eight subvoxels is generated, and then obtain the neighbors and blocks of subvoxels; otherwise obtain the neighbors and blocks at the voxel level. The default if `FALSE`.
`bias`	logical; if `TRUE`, the spatial parameters for biased field correction are calculated. The default if `FALSE`.

Value

A list containing the following components:

`neighbors`	a matrix, each row of which giving the neighbors of a voxel or subvoxel. The number of rows is equal to the number of (sub)voxels within the `mask` and the number of columns is the number of neighbors of each (sub)voxel. For the (sub)voxels on the boundaries, when one or more of their neighbors are missing, the missing are represented by the total number of (sub)voxels within the `mask` plus 1.
`blocks`	the (sub)voxels within each block are mutually independent given the (sub)voxels in other blocks.
`sub`	logical; the same as the input `sub`.
`subvox`	if `sub` is `TRUE`, it is a matrix, with each row giving the eight subvoxels of a voxel; otherwise it is equal to `NULL`.
`weights`	if `bias` is `TRUE`, it is a vector of weights of neighbors of every voxel for bias field correction; otherwise it is equal to `NULL`. The default is `NULL`.
`weineighbors`	if `bias` is `TRUE`, it is a vector of sum of weights of neighbors for bias field correction, one element per voxel; otherwise it is equal to `NULL`. The default is `NULL`.

References

Dai Feng, Dong Liang, and Luke Tierney (2013) An unified Bayesian hierarchical model for MRI tissue classification Statistics in Medicine

Dai Feng (2008) Bayesian Hidden Markov Normal Mixture Models with Application to MRI Tissue Classification Ph. D. Dissertation, The University of Iowa

Examples

  mask <- array(1, dim=c(2,2,2))
  spa <- makeMRIspatial(mask, nnei=6, sub=FALSE)
  spa <- makeMRIspatial(mask, nnei=6, sub=TRUE)
  spa <- makeMRIspatial(mask, nnei=26, sub=TRUE, bias=TRUE)
mask <- array(1, dim=c(2,2,2))
  spa <- makeMRIspatial(mask, nnei=6, sub=FALSE)
  spa <- makeMRIspatial(mask, nnei=6, sub=TRUE)
  spa <- makeMRIspatial(mask, nnei=26, sub=TRUE, bias=TRUE)

Compare the Predicted Classsification Results with the Truth

Description

Calculate and demonstrate different measures for classification results based on the truth.

Usage

   measureMRI(intvec, actual, pre)
measureMRI(intvec, actual, pre)

Arguments

`intvec`	a vector of intensity values. If it is not `NULL`, the density plots of each component corresponding to the actual and predicted classification results are shown. The default is `NULL`.
`actual`	matrix of the true classification result. Each row corresponds to one voxel. Column $i$ represents the probabilities that all voxels are allocated to tissue type $i$ .
`pre`	matrix of the predicted classification result. Each row corresponds to one voxel. Column $i$ represents the probabilities that all voxels are allocated to tissue type $i$ .

Value

`mse`	mean square error.
`misclass`	mis-classification rate.
`rseVolume`	root square error of volume with respect to reference tissue volume.
`DSM`	Dice Similary Measure of each tissue type. $DSM_{a,b}^{t}=\frac{2 \times N_{a \cap b}^t}{N_a^t+N_b^t}$ where $N_a^t$ and $N_b^t$ are the number of voxels classified as tissue type $t$ by method $a$ and $b$ respectively, and $N_{a \cap b}^t$ is the number of voxels classified as tissue type $t$ by both methods $a$ and $b$ . The larger the DSM, the more similar the results from the two methods.
`conTable`	confusion table. Each column of the table represents the instances in an actual class, while each row represents the instances in a predicted class.

Examples

  #Example 1
  prop <- c(.3, .4, .3)
  mu <- c(-4, 0, 4)
  sigma <- rep(1, 3)
  y <- rnormmix(n=1e4, prop, mu, sigma)
  intvec <- y[,1]
  actual <- y[,2]
  pre <- actual
  pre[sample(1:1e4, 100, replace=FALSE)]  <- sample(1:3, 100, replace=TRUE)
  actual <- do.call(cbind, lapply(1:3, function(i) ifelse(actual==i, 1, 0)))
  pre <- do.call(cbind, lapply(1:3, function(i) ifelse(pre==i, 1, 0)))
  measureMRI(intvec, actual, pre)

   
  #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")

  csf <- readMRI(system.file("extdata/csf.rawb.gz", package="mritc"),
                c(91,109,91), format="rawb.gz")
  gm <- readMRI(system.file("extdata/gm.rawb.gz", package="mritc"),
                c(91,109,91), format="rawb.gz")
  wm <- readMRI(system.file("extdata/wm.rawb.gz", package="mritc"),
                c(91,109,91), format="rawb.gz")
  truth <- cbind(csf[mask==1], gm[mask==1], wm[mask==1])
  truth <- truth/255
  measureMRI(T1[mask==1], truth, tc.icm$prob)
  
#Example 1
  prop <- c(.3, .4, .3)
  mu <- c(-4, 0, 4)
  sigma <- rep(1, 3)
  y <- rnormmix(n=1e4, prop, mu, sigma)
  intvec <- y[,1]
  actual <- y[,2]
  pre <- actual
  pre[sample(1:1e4, 100, replace=FALSE)]  <- sample(1:3, 100, replace=TRUE)
  actual <- do.call(cbind, lapply(1:3, function(i) ifelse(actual==i, 1, 0)))
  pre <- do.call(cbind, lapply(1:3, function(i) ifelse(pre==i, 1, 0)))
  measureMRI(intvec, actual, pre)

   
  #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")

  csf <- readMRI(system.file("extdata/csf.rawb.gz", package="mritc"),
                c(91,109,91), format="rawb.gz")
  gm <- readMRI(system.file("extdata/gm.rawb.gz", package="mritc"),
                c(91,109,91), format="rawb.gz")
  wm <- readMRI(system.file("extdata/wm.rawb.gz", package="mritc"),
                c(91,109,91), format="rawb.gz")
  truth <- cbind(csf[mask==1], gm[mask==1], wm[mask==1])
  truth <- truth/255
  measureMRI(T1[mask==1], truth, tc.icm$prob)

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 Markov chain Monte Carlo method (MCMC); the partial volume HMNMM fitted by the modified EM (PVHMRFEM) algorithm or the higher resolution HMNMM fitted by the MCMC method (MCMCsub); the HMNMM with both PV and intensity non-uniformity addressed (MCMCsubbias).

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,
               subbias, neighbors.bias, blocks.bias, weineighbors.bias, weights.bias,
               spatialMat, beta, mu, sigma, niter, verbose)
   mritc(intarr, mask, method)
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,
               subbias, neighbors.bias, blocks.bias, weineighbors.bias, weights.bias,
               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`, `mritc.icm`, `mritc.hmrfem`, and `mritc.pvhmrfem`), and proportions (for `mritc.em`) of all components of a normal mixture model. When it is a scalar, all have the same relative maximum error. The default value is 1e-4.
`maxit`	maximum number of iterations to perform. The default is 200 for `mritc.em`, 20 for `mritc.icm`, `mritc.hmrfem`, and `mritc.pvhmrfem`.
`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`, `mritc.hmrfem` and `mritc.bayes` when `sub` is `FALSE` and `matrix(c(2,0,-1,0,2,0,-1,0,2), nrow=3)` when `sub` is `TRUE`. For `mritc.pvhmrfem` the default is `matrix(c(2, 1, -1, -1, -1, 1, 2, 1, -1, -1, -1, 1, 2, 1, -1, -1, -1, 1, 2, 1, -1, -1, -1, 1, 2), ncol=5)`.
`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 `mritc.pvhmrfem`. For `mritc.bayes`, the default is 0.7 when `sub` is `FALSE` and 0.3 when `sub` is `TRUE`.
`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 `makeMRIspatial`. For the whole voxel method, `subvox`=`NULL`
`subbias`	logical; if `TRUE`, use the model that addresses both the PV and intensity non-uniformity. The default is `FALSE`.
`neighbors.bias`	a matrix of neighbors of bias field. One row per voxel. It can be obtained using the function `makeMRIspatial`. The default is `NULL`.
`blocks.bias`	blocks for bias field. It can be obtained using the function `makeMRIspatial`. The default is `NULL`.
`weineighbors.bias`	a vector of sum of weights of neighbors of bias field. One element per voxel. It can be obtained using the function `makeMRIspatial`. The default is `NULL`.
`weights.bias`	a vector of weights of different neighbors of every voxel. It can be obtained using the function `makeMRIspatial`. The default is `NULL`.
`niter`	the number of iterations for `mritc.bayes`. The default values are 1000 and 100 for with and without bias field correction, respectively. The default values seem 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", "MCMCsub", or "MCMCsubbias" corresponding to using the NMM fitted by the EM algorithm; the HMNMM fitted by the ICM algorithm, the HMRFEM algorithm, or the MCMC; the partial volume HMNMM fitted by the PVHMRFEM algorithm; the higher resolution HMNMM fitted by the MCMC; the HMNMN addressing both the PV and intensity non-uniformity. It can be abbreviated. The default is "EM".

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. For the bias field correction, the twenty-six neighbor structure is used and then the neighbors.bias, blocks.bias, weineighbors.bias and weights.bias are obtained using the function makeMRIspatial. The other parameters are taken as the default values for each method. The process is reported during iterations.

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 an 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.

Note

The functions support the various normal mixture models with at most eight components.

References

Julian Besag (1986) On the statistical analysis of dirty pictures (with discussion) Journal of the Royal Statistical Society. Series B (Methodological) vol. 48 259-302

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, Dong Liang, and Luke Tierney (2014) An unified Bayesian hierarchical model for MRI tissue classification Statistics in Medicine vol.33, issue 8 1349-1368

Dai Feng, Luke Tierney, and Vincent Magnotta (2012) MRI tissue classification using high resolution Bayesian hidden Markov normal mixture models Journal of the American Statistical Association, vol.107, no.497 102-119

Dai Feng and Luke Tierney (2011) mritc: A package for MRI tissue classification Journal of Statistical Software, vol.44, no.7 1-20 https://www.jstatsoft.org/v44/i07/

Dai Feng (2008) Bayesian hidden Markov normal mixture models with application to MRI tissue classification Ph. D. Dissertation, The University of Iowa

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

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)

  mrispatial26 <- makeMRIspatial(mask, nnei=26, sub=TRUE, bias=TRUE)
  tc.mcmcsubbias <- mritc.bayes(y, mrispatial$neighbors, mrispatial$blocks,
                                mrispatial$sub, mrispatial$subvox,
                                subbias=TRUE, mrispatial26$neighbors,
                                mrispatial26$blocks,mrispatial26$weineighbors,
                                mrispatial26$weights, 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")
#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)

  mrispatial26 <- makeMRIspatial(mask, nnei=26, sub=TRUE, bias=TRUE)
  tc.mcmcsubbias <- mritc.bayes(y, mrispatial$neighbors, mrispatial$blocks,
                                mrispatial$sub, mrispatial$subvox,
                                subbias=TRUE, mrispatial26$neighbors,
                                mrispatial26$blocks,mrispatial26$weineighbors,
                                mrispatial26$weights, 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")

Plot Method for Class "mritc"

Description

Visualize MRI tissue classification results.

Usage

   ## S3 method for class 'mritc'
plot(x, ...)
## S3 method for class 'mritc'
plot(x, ...)

Arguments

`x`	an object of class "mritc"
`...`	any additional arguments for function `slices3d`.

Details

Allocate a voxel to the tissue type with the highest probability and then use slices3d to show the result.

Value

NULL

Examples

  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")
  plot(tc.icm)
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")
  plot(tc.icm)

Print Method for Class "mritc"

Description

Print out some information of an object of class "mritc".

Usage

   ## S3 method for class 'mritc'
print(x, ...)
## S3 method for class 'mritc'
print(x, ...)

Arguments

`x`	an object of class "mritc".
`...`	any additional arguments.

Value

The function computes and returns some summary statistics of the object obtained from running the function mritc.

Examples

  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")
  tc.icm
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")
  tc.icm

Read an MR Image into an Array

Description

Read an MR image of different formats into an array.

Usage

   readMRI(file, dim, format)
readMRI(file, dim, format)

Arguments

`file`	the name of the image file to be read in.
`dim`	the dimensions of the image. It is required for the image of type `raw.gz`, in which `dim` is a vector of length three specifying dimensions in x, y, and z directions. The default is `NULL`.
`format`	the format of the image file. Right now only the "Analyze", "NIfTI", and raw byte (unsigned with 1 byte per element in the byte stream) gzip formats are supported.

Details

The files of "Analyze" format are read in through the function readANALYZE. The files of "NIfTI" format are read in through the functionreadNIfTI.

Value

An array with the appropriate dimensions containing the image volume.

Examples

  ## Not run: 
  vol1 <- readMRI("t1.rawb.gz", c(91,109,91), "rawb.gz")
  
  vol2 <- readMRI("t1.nii.gz", format="nifti")

  vol3  <- readMRI("t1.nii", format="nifti")

  vol4 <- readMRI("t1-analyze.img", format="analyze")
  
## End(Not run)
## Not run: 
  vol1 <- readMRI("t1.rawb.gz", c(91,109,91), "rawb.gz")
  
  vol2 <- readMRI("t1.nii.gz", format="nifti")

  vol3  <- readMRI("t1.nii", format="nifti")

  vol4 <- readMRI("t1-analyze.img", format="analyze")
  
## End(Not run)

Generate Random Samples from a Normal Mixture Model

Description

Generate random samples from a normal mixture model.

Usage

   rnormmix(n, prop, mu, sigma)
rnormmix(n, prop, mu, sigma)

Arguments

`n`	number of observations.
`prop`	a vector of proportions of different components.
`mu`	a vector of means of different components.
`sigma`	a vector of standard deviations of different components

Value

A matrix with each row corresponding to one sample. The first column are sample values from a normal mixture model; the second column are the components from which observations come.

Examples

  prop <- c(.17, .48, .35)
  mu <- c(-4, 0, 4)
  sigma <- rep(1, 3)
  y <- rnormmix(n=10000, prop, mu, sigma)
  densityplot(~ y[,1], groups = y[,2],
              plot.points = FALSE, ref = TRUE,
              xlab="sample values",
              auto.key = list(columns = 3))
prop <- c(.17, .48, .35)
  mu <- c(-4, 0, 4)
  sigma <- rep(1, 3)
  y <- rnormmix(n=10000, prop, mu, sigma)
  densityplot(~ y[,1], groups = y[,2],
              plot.points = FALSE, ref = TRUE,
              xlab="sample values",
              auto.key = list(columns = 3))

Summary Method for Class "mritc"

Description

Summarize some information of an object of class "mritc".

Usage

   ## S3 method for class 'mritc'
summary(object, ...)
## S3 method for class 'mritc'
summary(object, ...)

Arguments

`object`	an object of class "mritc".
`...`	any additional arguments.

Value

The function computes and returns some summary statistics of the object obtained from running the function mritc.

Examples

  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")
  summary(tc.icm)
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")
  summary(tc.icm)

Write an MR Image

Description

Write an MR image into a file of different formats.

Usage

   writeMRI(data, file, header, format)
writeMRI(data, file, header, format)

Arguments

`data`	MRI data in a three dimensional array or four dimensional array with the forth dimension equal to 1. It could be also an object of class "nifti" or "anlz" as defined in the package `oro.nifti`.
`file`	the name of the image file to be written out.
`header`	the header file. `header` is set as `NULL` for backward compatibility.
`format`	the format of the image file. Right now only the "Analyze", "NIfTI", and raw byte (unsigned with 1 byte per element in the byte stream) gzip formats are supported.

Details

Header file is not needed for the file of "Analyze" or "NIfTI" format anymore.

Files of "Analyze" format are written out through the function writeANALYZE. Files of "NIfTI" format are written out through the function writeNIfTI.

Value

Nothing is returned.

Examples

  ## Not run: 
  writeMRI(vol, file="vol.rawb.gz", header=NULL, format="rawb.gz")
  
  writeMRI(vol, file="vol", header=NULL, format="nifti")

  writeMRI(vol, file="vol", header=NULL, format="analyze")
  
## End(Not run)
## Not run: 
  writeMRI(vol, file="vol.rawb.gz", header=NULL, format="rawb.gz")
  
  writeMRI(vol, file="vol", header=NULL, format="nifti")

  writeMRI(vol, file="vol", header=NULL, format="analyze")
  
## End(Not run)

Package 'mritc'

Help Index

MRI Tissue Classification Package

Description

Introduction

Usage

Computation Issues

Estimate the Parameters of a Normal Mixture Model Using the EM Algorithm

Description

Usage

Arguments

Details

Value

Examples

Get the Initial Estimate of the Parameters of a Normal Mixture Model

Description

Usage

Arguments

Details

Value

Note

References

Examples

Obtain Spatial Features of a Mask of an MR Image

Description

Usage

Arguments

Value

References

Examples

Compare the Predicted Classsification Results with the Truth

Description

Usage

Arguments

Value

Examples

MRI Tissue Classification Using Various Methods

Description

Usage

Arguments

Details

Value

Note

References

Examples

Plot Method for Class "mritc"

Description

Usage

Arguments

Details

Value

See Also

Examples

Print Method for Class "mritc"

Description

Usage

Arguments

Value

Examples

Read an MR Image into an Array

Description

Usage

Arguments

Details

Value

See Also

Examples

Generate Random Samples from a Normal Mixture Model

Description

Usage

Arguments

Value

Examples

Summary Method for Class "mritc"

Description

Usage

Arguments

Value

Examples

Write an MR Image