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- Keyword
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- Autoradiography; Bayesian analysis; Image analysis; Markov random elds; RJMCMC; Swendsen-Wang algorithm (1)
- Bayesian hierarchical model; Functional brain mapping; Multi-subject fMRI data analysis; Reversible jump Markov chain Monte Carlo; Spatial mixture model (1)
- EM algorithm; Hidden Markov Random Field; Missing Data; Model Selection; Quantitative MRI; Stochastic Variation (1)
Articles 1 - 3 of 3
Full-Text Articles in Statistical Models
Bayesian Bivariate Image Analysis With Application To Dual Autoradiography, Timothy D. Johnson, Morand Piert
Bayesian Bivariate Image Analysis With Application To Dual Autoradiography, Timothy D. Johnson, Morand Piert
The University of Michigan Department of Biostatistics Working Paper Series
We present a Bayesian bivariate image model and apply it to a study that was designed to investigate the relationship between hypoxia and angiogenesis in an animal tumor model. Two radiolabeled tracers (one measuring angio- genesis, the other measuring hypoxia) were simultaneously injected into the animals, the tumors removed and autoradiographic images of the tracer concentrations were obtained. We model correlation between tracers with a mixture of bivariate normal distributions and the spatial correlation inherent in the images by means of the celebrated Potts model. Although the Potts model is typically used for image segmentation, we use it solely as …
Quantitative Magnetic Resonance Image Analysis Via The Em Algorithm With Stochastic Variation, Xiaoxi Zhang, Timothy D. Johnson, Roderick J.A. Little
Quantitative Magnetic Resonance Image Analysis Via The Em Algorithm With Stochastic Variation, Xiaoxi Zhang, Timothy D. Johnson, Roderick J.A. Little
The University of Michigan Department of Biostatistics Working Paper Series
Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which, researchers hope to predict (local) therapeutic efficacy early and determine optimal treatment schedule. However, the analysis of qMRI has been limited to ad-hoc heuristic methods. Our research provides a powerful statistical framework for image analysis and sheds light on future localized adaptive treatment regimes tailored to the individual’s response. We assume in an imperfect world we only observe a blurred and noisy version of the underlying “true” scene via qMRI, due to measurement errors or unpredictable influences. We use …
Bayesian Spatial Modeling Of Fmri Data: A Multiple-Subject Analysis, Lei Xu, Timothy Johnson, Thomas Nichols
Bayesian Spatial Modeling Of Fmri Data: A Multiple-Subject Analysis, Lei Xu, Timothy Johnson, Thomas Nichols
The University of Michigan Department of Biostatistics Working Paper Series
The aim of this work is to develop a spatial model for multi-subject fMRI data. While there has been much work on univariate modeling of each voxel for single- and multi-subject data, and some work on spatial modeling for single-subject data, there has been no work on spatial models that explicitly account for intersubject variability in activation location. We use a Bayesian hierarchical spatial model to fit the data. At the first level we model "population centers" that mark the centers of regions of activation. For a given population center each subject may have zero or more associated "individual components". …