Mixture models are often used in the statistical segmentation of medical images. For example, they can be used for the segmentation of structural images into different matter types or of functional statistical parametric maps (SPMs) into activations and nonactivations. Nonspatial mixture models segment using models of just the histogram of intensity values. Spatial mixture models have also been developed which augment this histogram information with spatial regularization using Markov random fields. However, these techniques have control parameters, such as the strength of spatial regularization, which need to be tuned heuristically to particular datasets. We present a novel spatial mixture model within a fully Bayesian framework with the ability to perform fully adaptive spatial regularization using Markov random fields. This means that the amount of spatial regularization does not have to be tuned heuristically but is adaptively determined from the data. We examine the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs.

Type

Journal article

Journal

IEEE Trans Med Imaging

Publication Date

01/2005

Volume

24

Pages

1 - 11

Keywords

Algorithms, Artificial Intelligence, Brain, Cluster Analysis, Computer Simulation, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Information Storage and Retrieval, Magnetic Resonance Imaging, Models, Biological, Models, Statistical, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted