Journal ArticleDOI
Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data
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TLDR
This work presents a novel spatial mixture model within a fully Bayesian framework with the ability to perform fully adaptive spatial regularization using Markov random fields, and examines the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs.Abstract:
Mixture models are often used in the statistical segmentation of medical images. For example, they can be used for the segmentation of structural images into different matter types or of functional statistical parametric maps (SPMs) into activations and nonactivations. Nonspatial mixture models segment using models of just the histogram of intensity values. Spatial mixture models have also been developed which augment this histogram information with spatial regularization using Markov random fields. However, these techniques have control parameters, such as the strength of spatial regularization, which need to be tuned heuristically to particular datasets. We present a novel spatial mixture model within a fully Bayesian framework with the ability to perform fully adaptive spatial regularization using Markov random fields. This means that the amount of spatial regularization does not have to be tuned heuristically but is adaptively determined from the data. We examine the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs.read more
Citations
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Journal ArticleDOI
Advances in functional and structural MR image analysis and implementation as FSL.
Stephen M. Smith,Mark Jenkinson,Mark W. Woolrich,Mark W. Woolrich,Christian F. Beckmann,Behrens Tej.,Heidi Johansen-Berg,Peter R. Bannister,M De Luca,Ivana Drobnjak,D E Flitney,Rami K. Niazy,J Saunders,J Vickers,Yongyue Zhang,N. De Stefano,J M Brady,Paul M. Matthews +17 more
TL;DR: A review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB) on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data.
Journal ArticleDOI
New advances in the Clinica software platform for clinical neuroimaging studies
Alexandre Routier,Arnaud Marcoux,Mauricio Diaz Melo,Jérémy Guillon,Jorge Samper-González,Junhao Wen,Simona Bottani,Alexis Guyot,Elina Thibeau-Sutre,Marc Teichmann,Marie-Odile Habert,Stanley Durrleman,Ninon Burgos,Olivier Colliot +13 more
Journal ArticleDOI
Circular analysis in systems neuroscience: the dangers of double dipping.
TL;DR: It is argued that systems neuroscience needs to adjust some widespread practices to avoid the circularity that can arise from selection, and 'double dipping' the use of the same dataset for selection and selective analysis is suggested.
Journal ArticleDOI
Bayesian analysis of neuroimaging data in FSL.
Mark W. Woolrich,Saâd Jbabdi,Brian Patenaude,Michael A. Chappell,Salima Makni,Timothy E.J. Behrens,Christian F. Beckmann,Christian F. Beckmann,Mark Jenkinson,Stephen M. Smith +9 more
TL;DR: How Bayesian techniques have made a significant impact in tackling problems such as neuroimaging problems, particularly in regards to the analysis tools in the FMRIB Software Library (FSL), is described.
References
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Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Journal ArticleDOI
Statistical parametric maps in functional imaging: A general linear approach
Karl J. Friston,Andrew P. Holmes,Keith J. Worsley,J-B. Poline,Chris D. Frith,Richard S. J. Frackowiak +5 more
TL;DR: In this paper, the authors present a general approach that accommodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors).
Journal ArticleDOI
Improved optimization for the robust and accurate linear registration and motion correction of brain images
Book
Statistics for spatial data
Noel A Cressie,Noel A Cressie +1 more
TL;DR: In this paper, the authors present a survey of statistics for spatial data in the field of geostatistics, including spatial point patterns and point patterns modeling objects, using Lattice Data and spatial models on lattices.
BookDOI
Markov Chain Monte Carlo in Practice
TL;DR: The Markov Chain Monte Carlo Implementation Results Summary and Discussion MEDICAL MONITORING Introduction Modelling Medical Monitoring Computing Posterior Distributions Forecasting Model Criticism Illustrative Application Discussion MCMC for NONLINEAR HIERARCHICAL MODELS.