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Proceedings Paper

Semi-automated segmentation of cortical subvolumes via hierarchical mixture modeling
Author(s): J. Tilak Ratnanather; Carey E. Priebe; Michael I. Miller
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Paper 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).

Paper Details

Date Published: 15 May 2003
PDF: 11 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481363
Show Author Affiliations
J. Tilak Ratnanather, Johns Hopkins Univ. (United States)
Carey E. Priebe, Johns Hopkins Univ. (United States)
Michael I. Miller, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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