scholarly journals Quantification of Myocardial Perfusion Lesions using Spatially Variant Finite Mixture Modelling of DCE-MRI

2019 ◽  
Author(s):  
Yalei Yang ◽  
Hao Gao ◽  
Colin Berry ◽  
Aleksandra Radjenovic ◽  
Dirk Husmeier
Keyword(s):  
Myocardial Perfusion ◽  
Finite Mixture ◽  
Dce Mri ◽  
Mixture Modelling ◽  
Spatially Variant

scholarly journals A Finite Mixture Modelling Perspective for Combining Experts’ Opinions with an Application to Quantile-Based Risk Measures

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 115
Author(s):  
Despoina Makariou ◽  
Pauline Barrieu ◽  
George Tzougas
Keyword(s):  
Decision Making ◽  
Mixture Models ◽  
Finite Mixture Models ◽  
Risk Measures ◽  
Finite Mixture ◽  
Parametric Family ◽  
Collective Decision Making ◽  
Multiple Sources ◽  
Mixture Modelling ◽  
Expert Opinions

The key purpose of this paper is to present an alternative viewpoint for combining expert opinions based on finite mixture models. Moreover, we consider that the components of the mixture are not necessarily assumed to be from the same parametric family. This approach can enable the agent to make informed decisions about the uncertain quantity of interest in a flexible manner that accounts for multiple sources of heterogeneity involved in the opinions expressed by the experts in terms of the parametric family, the parameters of each component density, and also the mixing weights. Finally, the proposed models are employed for numerically computing quantile-based risk measures in a collective decision-making context.

scholarly journals First-pass myocardial perfusion assessment using eight-fold accelerated k-t BLAST stress DCE-MRI with rapid parametric mapping

2009 ◽  
Vol 11 (S1) ◽  
Author(s):  
Aleksandra Radjenovic ◽  
Sven Plein ◽  
Neil Maredia ◽  
Sebastian Kozerke ◽  
John Biglands ◽  
...  
Keyword(s):  
Myocardial Perfusion ◽  
Parametric Mapping ◽  
Dce Mri ◽  
First Pass ◽  
Perfusion Assessment

Statistical analysis of sheep CAT scan images using a Bayesian mixture model

2004 ◽  
Vol 55 (1) ◽  
pp. 57 ◽  
Author(s):  
C. L. Alston ◽  
K. L. Mengersen ◽  
J. M. Thompson ◽  
P. J. Littlefield ◽  
D. Perry ◽  
...  
Keyword(s):  
Animal Experiments ◽  
Finite Mixture ◽  
Lean Tissue ◽  
Gaussian Distributions ◽  
Mixture Modelling ◽  
Bayesian Mixture Model ◽  
Cat Scan ◽  
Distributional Properties ◽  
Bayesian Formulation ◽  
Bayesian Mixture

CAT scanning techniques are available to provide images that can aid in the assessment of carcass traits in live sheep during the course of animal experiments. In this paper we present a Bayesian formulation of an analysis that allows us to determine the composition of a scan in terms of proportions of the image attributable to fat, muscle (lean tissue), and bone. The technique, known as finite mixture modelling, also provides information about the distributional properties of some of these components, such as fat and bone. In the case of muscle, the analysis estimates several Gaussian distributions that combine to provide an approximation to its likelihood.The model is estimated through the use of the Gibbs sampler, with the distributional properties of carcass components being obtained from the resultant Markov chains.

Sign in / Sign up

Export Citation Format

Share Document