A Model for Segmentation and Analysis of Noisy Images

Abstract

This article proposes a statistical model for image generation that provides automatic segmentation of images into intensity-differentiated regions and facilitates the quantitative assessment of uncertainty associated with identified image features. The model is specified hierarchically within the Bayesian paradigm. At the lowest level in the hierarchy, a Gibbs distribution is used to specify a probability distribution on the space of all possible partitions of the discretized image scene. An important feature of this distribution is that the number of partitioning elements, or image regions, is not specified a priori. At higher levels in the hierarchical specification, random variables representing emission intensities are associated with regions and pixels. Observations are assumed to be generated from exponential family models centered about these values.

Key Words:

• Bayesian inference
• Emission computed tomography
• Gibbs distributions
• Hexagonal arrays
• Hierarchical models
• Image reconstruction
• Image restoration