PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Tissue classification of noisy MR brain images using constrained GMM
Amit Ruf, Hayit Greenspan and Jacob Goldberger
In: MICCAI 2005, 26-30 Oct 2005, California, USA.

Abstract

We present an automated algorithm for tissue segmentation of noisy, low contrast magnetic resonance (MR) images of the brain. We use a mixture model composed of a large number of Gaussians, with each brain tissue represented by a large number of the Gaussian components in order to capture the complex tissue spatial layout. The intensity of a tissue is considered a global feature and is incorporated into the model through parameter tying of all the related Gaussians. The EM algorithm is utilized to learn the parameter-tied Gaussian mixture model. A new initialization method is applied to guarantee the convergence of the EM algorithm to the global maximum likelihood. Segmentation of the brain image is achieved by the affiliation of each voxel to a selected tissue class. The presented algorithm is used to segment 3D, T1--weighted, simulated and real MR images of the brain into three different tissues, under varying noise conditions. Quantitative results are compared with other segmentation results reported in the literature.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Learning/Statistics & Optimisation
ID Code:1561
Deposited By:Jacob Goldberger
Deposited On:28 November 2005