Parameter estimation and tissue segmentation from multispectral MR images

TLDR

The method assumes that tissue‑specific image intensities can be modeled as a multivariate likelihood of T1, T2, and proton density values and that tissue regions are piecewise contiguous, described by a Markov random field prior. The authors develop a statistical approach to classify tissue types and segment their regions from T1, T2, and proton density weighted MR images. They model the likelihood as a finite multivariate mixture, estimate class parameters and the number of classes by maximum likelihood and minimum description length, and segment tissue regions by maximum a posteriori probability using a Markov random field prior, validated on 1.5‑T brain scans. The method automatically estimates the number of classes and class parameters via expectation‑maximization, yielding satisfactory segmentation of different brain tissues.

Abstract

A statistical method is developed to classify tissue types and to segment the corresponding tissue regions from relaxation time T(1 ), T(2), and proton density P(D) weighted magnetic resonance images. The method assumes that the distribution of image intensities associated with each tissue type can be expressed as a multivariate likelihood function of three weighted signal intensity values (T(1), T(2), P(D)) at each location within that tissue regions. The method further assumes that the underlying tissue regions are piecewise contiguous and can be characterized by a Markov random field prior. In classifying the tissue types, the method models the likelihood of realizing the images as a finite multivariate-mixture function. The class parameters associated with the tissue types (i.e. the weighted intensity means, variances and correlation coefficients of the multivariate function, as well as the number of voxels within regions of the tissue types of are estimated by maximum likelihood. The estimation fits the class parameters to the image data via the expectation-maximization algorithm. The number of classes associated with the tissue types is determined by the information criterion of minimum description length. The method segments the tissue regions, given the estimated class parameters, by maximum a posteriori probability. The prior is constructed by the tissue-region membership of the first- and second-order neighborhood. The method is tested by a few sets of T(1), T(2), and P(D) weighted images of the brain acquired with a 1.5 Tesla whole body scanner. The number of classes and the associated class parameters are automatically estimated. The regions of different brain tissues are satisfactorily segmented.