Semi-automated segmentation of cortical subvolumes via hierarchical mixture modeling

J. Tilak Ratnanather; Carey E. Priebe; Michael I. Miller

doi:10.1117/12.481363

Abstract

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 "alternatinv 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 a superior automatic segmentation results with a performance improvement that is statistically significant (p=0.03 for a paired one-sided t-test).

Citation Download Citation

J. Tilak Ratnanather, J. Tilak Ratnanather, Carey E. Priebe, Carey E. Priebe, Michael I. Miller, Michael I. Miller, "Semi-automated segmentation of cortical subvolumes via hierarchical mixture modeling", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481363; https://doi.org/10.1117/12.481363

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