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Richard M. Leahy

Researcher at University of Southern California

Publications -  419
Citations -  27317

Richard M. Leahy is an academic researcher from University of Southern California. The author has contributed to research in topics: Iterative reconstruction & Imaging phantom. The author has an hindex of 70, co-authored 406 publications receiving 24876 citations. Previous affiliations of Richard M. Leahy include Los Alamos National Laboratory & Johns Hopkins University School of Medicine.

Papers
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Journal ArticleDOI

Brainstorm: a user-friendly application for MEG/EEG analysis

François Tadel, +4 more
- 01 Jan 2011 - 
TL;DR: Brainstorm as discussed by the authors is a collaborative open-source application dedicated to magnetoencephalography (MEG) and EEG data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data.
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Electromagnetic brain mapping

Sylvain Baillet, +2 more
- 01 Nov 2001 - 
TL;DR: The underlying models currently used in MEG/EEG source estimation are described and the various signal processing steps required to compute these sources are described.
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An optimal graph theoretic approach to data clustering: theory and its application to image segmentation

Z. Wu, +1 more
- 01 Nov 1993 - 
TL;DR: A novel graph theoretic approach for data clustering is presented and its application to the image segmentation problem is demonstrated, resulting in an optimal solution equivalent to that obtained by partitioning the complete equivalent tree and is able to handle very large graphs with several hundred thousand vertices.
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Multiple dipole modeling and localization from spatio-temporal MEG data

John C. Mosher, +2 more
- 01 Jun 1992 - 
TL;DR: The authors present general descriptive models for spatiotemporal MEG (magnetoencephalogram) data and show the separability of the linear moment parameters and nonlinear location parameters in the MEG problem and present a subspace methodology and computational approach to solving the conventional least-squares problem.
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Magnetic resonance image tissue classification using a partial volume model.

David W. Shattuck, +5 more
- 01 May 2001 - 
TL;DR: A sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI) using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology is described.