Analysis and Reconstruction of Medical Images Using Prior Information

Valen Johnson, James Bowsher, Timothy Turkington, Ronald Jaszczak
Duke University

Nov 30 1992

We propose a Bayesian model for medical image analysis that permits prior structural information to be incorporated into the estimation of image features. Although the proposed methodology can be applied to a broad spectrum of images, we restrict attention here to emission computer tomography (ECT) images, and in particular single photon emission computer tomography (SPECT) images.

Inclusion of prior information regarding likely shapes of objects in the source distribution is accomplished using a hierarchical Bayesian model. A distinguishing feature of this model is that at the lowest level of the hierarchy, a distribution is specified over the class of all partitions of the discrestized image scene. The markers used to identify these regions, called region identifiers, are assigned to each voxel (i.e. a volume element, similar to a pixel in 2D images), and are the mechanism by which information regarding the locations of likely image structure is incorporated into the prior model. Importantly, such information can be incorporated in a non-deterministic fashion, thus permitting prior structural information to be modified by image data with minimal introduction of residual artifacts. Furthermore, the statistical model accommodates the formation of object structures not anticipated a priori, based solely on the observed likelihood function.


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