4,861 research outputs found
Flexible Bayesian Modelling for Nonlinear Image Registration
We describe a diffeomorphic registration algorithm that allows groups of
images to be accurately aligned to a common space, which we intend to
incorporate into the SPM software. The idea is to perform inference in a
probabilistic graphical model that accounts for variability in both shape and
appearance. The resulting framework is general and entirely unsupervised. The
model is evaluated at inter-subject registration of 3D human brain scans. Here,
the main modeling assumption is that individual anatomies can be generated by
deforming a latent 'average' brain. The method is agnostic to imaging modality
and can be applied with no prior processing. We evaluate the algorithm using
freely available, manually labelled datasets. In this validation we achieve
state-of-the-art results, within reasonable runtimes, against previous
state-of-the-art widely used, inter-subject registration algorithms. On the
unprocessed dataset, the increase in overlap score is over 17%. These results
demonstrate the benefits of using informative computational anatomy frameworks
for nonlinear registration.Comment: Accepted for MICCAI 202
Review of the mathematical foundations of data fusion techniques in surface metrology
The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed
Most Likely Separation of Intensity and Warping Effects in Image Registration
This paper introduces a class of mixed-effects models for joint modeling of
spatially correlated intensity variation and warping variation in 2D images.
Spatially correlated intensity variation and warp variation are modeled as
random effects, resulting in a nonlinear mixed-effects model that enables
simultaneous estimation of template and model parameters by optimization of the
likelihood function. We propose an algorithm for fitting the model which
alternates estimation of variance parameters and image registration. This
approach avoids the potential estimation bias in the template estimate that
arises when treating registration as a preprocessing step. We apply the model
to datasets of facial images and 2D brain magnetic resonance images to
illustrate the simultaneous estimation and prediction of intensity and warp
effects
Semiparametric Bayesian Time-Space Analysis of Unemployment Duration
In this paper, we analyze unemployment duration in Germany with official data from the German Federal Employment Office for the years 1980-1995. Conventional hazard rate models for leaving unemployment cannot cope with simultaneous and flexible fitting of duration dependence, nonlinear covariate effects, trend and seasonal calendar time components and a large number of regional effects. We apply a semiparametric hierarchical Bayesian modelling approach that is suitable for time-space analysis of unemployment duration by simultaneously including and estimating effects of several time scales, regional variation and further covariates. Inference is fully Bayesian and uses recent Markov chain Monte Carlo techniques
Joint Clustering and Registration of Functional Data
Curve registration and clustering are fundamental tools in the analysis of
functional data. While several methods have been developed and explored for
either task individually, limited work has been done to infer functional
clusters and register curves simultaneously. We propose a hierarchical model
for joint curve clustering and registration. Our proposal combines a Dirichlet
process mixture model for clustering of common shapes, with a reproducing
kernel representation of phase variability for registration. We show how
inference can be carried out applying standard posterior simulation algorithms
and compare our method to several alternatives in both engineered data and a
benchmark analysis of the Berkeley growth data. We conclude our investigation
with an application to time course gene expression
Semiparametric Bayesian models for human brain mapping
Functional magnetic resonance imaging (fMRI) has led to enormous progress in human brain mapping. Adequate analysis of the massive spatiotemporal data sets generated by this imaging technique, combining parametric and non-parametric components, imposes challenging problems in statistical modelling. Complex hierarchical Bayesian models in combination with computer-intensive Markov chain Monte Carlo inference are promising tools.The purpose of this paper is twofold. First, it provides a review of general semiparametric Bayesian models for the analysis of fMRI data. Most approaches focus on important but separate temporal or spatial aspects of the overall problem, or they proceed by stepwise procedures. Therefore, as a second aim, we suggest a complete spatiotemporal model for analysing fMRI data within a unified semiparametric Bayesian framework. An application to data from a visual stimulation experiment illustrates our approach and demonstrates its computational feasibility
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
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