Difference between revisions of "Projects:ParticlesForShapesAndComplexes"
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This work addresses technical challenges in biomedical shape analysis through the development of novel modeling and analysis methodologies, and seeks validation of those methodologies by their application to real-world research problems. The main focus of the work is the development and validation of a new framework for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The proposed optimization uses an entropy-based minimization that balances the | This work addresses technical challenges in biomedical shape analysis through the development of novel modeling and analysis methodologies, and seeks validation of those methodologies by their application to real-world research problems. The main focus of the work is the development and validation of a new framework for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The proposed optimization uses an entropy-based minimization that balances the | ||
simplicity of the model (compactness) with the accuracy of the surface representations. This framework is easily extended to handle more general classes of shape modeling, such as multiple-object complexes and correspondence | simplicity of the model (compactness) with the accuracy of the surface representations. This framework is easily extended to handle more general classes of shape modeling, such as multiple-object complexes and correspondence | ||
| − | based on | + | based on ''functions'' of position. This work also addresses the issue of how to do hypothesis testing with the proposed modeling framework, since, to date, the shape analysis community has not reached a consensus regarding a systematic approach to statistical analysis with point-based models. Finally, another important issue that remains is how to ''visualize'' significant shape differences in a way that allows researchers to understand not only whether differences exist, but what those shape differences are. This latter consideration is obviously of importance in in relating shape differences to scientific hypotheses. |
The following list is a summary of research and development results to date. | The following list is a summary of research and development results to date. | ||
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*The particle-based correspondence algorithm and statistical analysis methodology have been extended to more general classes of shape analysis problems: (a) the analysis of multiple-object complexes and (b) the generalization to correspondences based on generic functions of position. | *The particle-based correspondence algorithm and statistical analysis methodology have been extended to more general classes of shape analysis problems: (a) the analysis of multiple-object complexes and (b) the generalization to correspondences based on generic functions of position. | ||
*In cooperation with scientists and clinicians, we have published several papers that evaluate the above methodologies in the context of biomedical research. | *In cooperation with scientists and clinicians, we have published several papers that evaluate the above methodologies in the context of biomedical research. | ||
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= Key Investigators = | = Key Investigators = | ||
Revision as of 23:09, 1 October 2008
Home < Projects:ParticlesForShapesAndComplexesBack to NA-MIC Collaborations, Utah Algorithms
Shape Modeling and Analysis with Particle Systems
Overview
This work addresses technical challenges in biomedical shape analysis through the development of novel modeling and analysis methodologies, and seeks validation of those methodologies by their application to real-world research problems. The main focus of the work is the development and validation of a new framework for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The proposed optimization uses an entropy-based minimization that balances the simplicity of the model (compactness) with the accuracy of the surface representations. This framework is easily extended to handle more general classes of shape modeling, such as multiple-object complexes and correspondence based on functions of position. This work also addresses the issue of how to do hypothesis testing with the proposed modeling framework, since, to date, the shape analysis community has not reached a consensus regarding a systematic approach to statistical analysis with point-based models. Finally, another important issue that remains is how to visualize significant shape differences in a way that allows researchers to understand not only whether differences exist, but what those shape differences are. This latter consideration is obviously of importance in in relating shape differences to scientific hypotheses.
The following list is a summary of research and development results to date.
- We have implemented a mathematical framework and a robust numerical algorithm implementation for computing optimized correspondence-point shape models using an entropy-based optimization and particle-system technology.
- We have proposed a general methodology for hypothesis testing with point-based shape models that is suitable for use with the particle-based correspondence algorithm. Additionally, we have proposed ideas for visualization to aid in the interpretation of these shape statistics.
- The particle-based correspondence algorithm and statistical analysis methodology have been extended to more general classes of shape analysis problems: (a) the analysis of multiple-object complexes and (b) the generalization to correspondences based on generic functions of position.
- In cooperation with scientists and clinicians, we have published several papers that evaluate the above methodologies in the context of biomedical research.
Key Investigators
Josh Cates, Tom Fletcher, Ross Whitaker
Publications
In Print
