Stochastic representation of memoryless Boolean functions: application to boundary estimation at low contrast

Earlier work on parallel computation of generalized Bayesian hypothesis tests for hierarchical image reconstruction on massively parallel processor arrays is extended to incorporate pattern constraints specified with Boolean functions defined on symbolic imaging variables. This is based on a stochastic representation for memoryless Boolean functions following U. Grenander's work (1984) on metric pattern theory. Its application is presented to the segmentation of low-contrast textured images through an extension of J. Besag's (1986) ICM segmentation algorithm, and to image reconstruction with large point spread. Hierarchical image reconstruction in time-of-flight positron emission tomography at low count levels is described.<
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