Estimation of Response Functions Based on Variational Bayes Algorithm in Dynamic Images Sequences.
We proposed a nonparametric Bayesian model based on variational Bayes algorithm to estimate the response functions in dynamic medical imaging. In dynamic renal scintigraphy, the impulse response or retention functions are rather complicated and finding a suitable parametric form is problematic. In t...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 10 |
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| Autor principal: | |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/18/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | We proposed a nonparametric Bayesian model based on variational Bayes algorithm to estimate the response functions in dynamic medical imaging. In dynamic renal scintigraphy, the impulse response or retention functions are rather complicated and finding a suitable parametric form is problematic. In this paper, we estimated the response functions using nonparametric Bayesian priors. These priors were designed to favor desirable properties of the functions, such as sparsity or smoothness. These assumptions were used within hierarchical priors of the variational Bayes algorithm. We performed our algorithm on the real online dataset of dynamic renal scintigraphy. The results demonstrated that this algorithm improved the estimation of response functions with nonparametric priors. |
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