S
Stephen M. Smith
Researcher at University of Oxford
Publications - 529
Citations - 165046
Stephen M. Smith is an academic researcher from University of Oxford. The author has contributed to research in topics: Resting state fMRI & Human Connectome Project. The author has an hindex of 128, co-authored 501 publications receiving 140104 citations. Previous affiliations of Stephen M. Smith include Max Planck Society & Wellcome Trust Centre for Neuroimaging.
Papers
More filters
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
Improved optimization for the robust and accurate linear registration and motion correction of brain images
Journal ArticleDOI
A global optimisation method for robust affine registration of brain images
Mark Jenkinson,Stephen M. Smith +1 more
TL;DR: It is demonstrated that the use of local optimisation methods together with the standard multi-resolution approach is not sufficient to reliably find the global minimum, so a global optimisation method is proposed that is specifically tailored to this form of registration.
Journal ArticleDOI
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
TL;DR: 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.