Semi-automated segmentation of cortical subvolumes via hierarchical mixture modelling

We propose a method which allows for the flexibility of a Gaussian mixture model - with model complexity selected adaptively from the data - for each tissue class. Our procedure involves modelling each class as a semiparametric mixture of Gaussians. The major difficulty associated with employing such semiparametric methods is overcome by solving dynamically the model selection problem. The crucial step of determining class-conditional mixture complexities for (unlabeled) test data in the unsupervised case is accomplished by matching models to a predefined data base of hand labelled experimental tissue samples. We model the class-conditional probability density functions via the "alternating kernel and mixture" (AKM) method which involves (1) semi-parametric estimation of subject-specific class-conditional marginal densities for a set of training volumes, (2) nearest neighbor matching of the test data to the training models providing for semi-automated class-conditional mixture complexities, (3) parameter fitting of the selected training model to the test data, and (4) plug-in Bayes classification of unlabeled voxels. Compared with previous approaches using partial volume mixtures for ten cingulate gyri, the hierarchical mixture model methodology provides superior automatic segmentation results with a performance improvement that is statistically significant (p = 0.03 for a paired one-sided t-test).