Journal ArticleDOI
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
TLDR
The authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations.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
Fast robust automated brain extraction
TL;DR: An automated method for segmenting magnetic resonance head images into brain and non‐brain has been developed and described and examples of results and the results of extensive quantitative testing against “gold‐standard” hand segmentations, and two other popular automated methods.
Journal ArticleDOI
Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.
Bruce Fischl,David H. Salat,Evelina Busa,Marilyn S. Albert,Megan E. Dieterich,Christian Haselgrove,Andre van der Kouwe,Ronald J. Killiany,David N. Kennedy,Shuna Klaveness,Albert Montillo,Nikos Makris,Bruce R. Rosen,Anders M. Dale +13 more
TL;DR: In this paper, a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set is presented.
Journal ArticleDOI
The minimal preprocessing pipelines for the Human Connectome Project.
Matthew F. Glasser,Stamatios N. Sotiropoulos,J. Anthony Wilson,Timothy S. Coalson,Bruce Fischl,Jesper L. R. Andersson,Junqian Xu,Saâd Jbabdi,Matthew A. Webster,Jonathan R. Polimeni,David C. Van Essen,Mark Jenkinson +11 more
TL;DR: The minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space are described.
Journal ArticleDOI
Automatically Parcellating the Human Cerebral Cortex
Bruce Fischl,Andre van der Kouwe,Christophe Destrieux,Eric Halgren,Florent Ségonne,David H. Salat,Evelina Busa,Larry J. Seidman,Jill M. Goldstein,David N. Kennedy,Verne S. Caviness,Nikos Makris,Bruce R. Rosen,Anders M. Dale +13 more
TL;DR: A technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set is presented, comparable in accuracy to manual labeling.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
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.
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Advances in functional and structural MR image analysis and implementation as FSL.
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