A framework for incorporating structural prior information into the estimation of medical images

Abstract

I propose a Bayesian model for medical image analysis that permits prior structural information to be incorporated into the estimation of image features. Inclusion of prior information is accomplished using the image model described in [7]. A distinguishing feature of this model is the specification of a hierarchical structure for image generation that explicitly incorporates region parameters. Importantly, these region identifiers allow prior information to be incorporated in a nondeterministic fashion, thus permitting prior structural information to be modified by image data with minimal introduction of residual artifacts. Furthermore, the resulting statistical model permits formation of previously unidentified structures based on the observed data likelihood.