Probabilistic Constraint Satisfaction with Structural Models: Application to Organ Modeling by Radial Contours

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Citation: Russ B. Altman and James F. Brinkley. (1993) Probabilistic Constraint Satisfaction with Structural Models: Application to Organ Modeling by Radial Contours. In KSL-93-33, 1993.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Russ B. Altman and James F. Brinkley
title Probabilistic Constraint Satisfaction with Structural Models: Application to Organ Modeling by Radial Contours
number KSL-93-33
institution Knowledge Systems, AI Laboratory
address Washington D.C.
year 1993
Bibtex more
note April Updated August 1993.
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abstract 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.

KSL Technical Report ID: KSL-93-33
Facts about Probabilistic Constraint Satisfaction with Structural Models: Application to Organ Modeling by Radial ContoursRDF feed
Abstract One of the key challenges within medical i … 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. ructural constraint satisfaction problems.
Address Washington D.C.  +
Author Russ B. Altman and James F. Brinkley  +
Bibtype techreport  +
Has author Russ B. Altman and James F. Brinkley  +
Has identifier KSL-93-33  +
Has publishing details 1993  +
Has title Probabilistic Constraint Satisfaction with Structural Models: Application to Organ Modeling by Radial Contours  +
Has where published KSL-93-33  +
Has year 1993  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-93-33  +
Note April Updated August 1993.
Number KSL-93-33  +
Process note YES  +
Title Probabilistic Constraint Satisfaction with Structural Models: Application to Organ Modeling by Radial Contours  +
Year 1993  +
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