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Proc Annu Symp Comput Appl Med Care. 1993 : 492–496.
PMCID: PMC2850626

Probabilistic constraint satisfaction with structural models: application to organ modeling by radial contours.


One of the key challenges within medical information sciences is the development of useful models for biological structure and its variability. Many biomedical problems involve the elucidation of structure (for example, from experimental data or from imaging studies), and structural models can often drive the process of inferring precise structure from data. Ideally, model-driven data interpretation combines knowledge about the generic features of a class of biological structures (as contained within a model) with data that provide specific information (often noisy) about a particular instance of the class. In this paper we briefly discuss model-driven determination of biological structure as an example of a structural constraint satisfaction problem. We describe a probabilistic implementation of structural constraint satisfaction, and show that our formulation of a particular organ modeling technology (Radial Contour Models) exhibits promising performance. Our results demonstrate the utility of probabilistic models for the solution of structural constraint satisfaction problems.

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Selected References

These references are in PubMed. This may not be the complete list of references from this article.
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Articles from Proceedings of the Annual Symposium on Computer Application in Medical Care are provided here courtesy of American Medical Informatics Association