Bibliothèque de L'IMIST/CNRST

Brain Warping is the premier book in the field of brain mapping to cover the mathematics, physics, computer science, and neurobiological issues related to brain spatial transformation and deformation correction. All chapters are organized in a similar fashion, covering the history, theory, and implementation of the specific approach discussed for ease of reading. Each chapter also discusses the computer science implementations, including descriptions of the programs and computer codes used in its execution. Readers of Brain Warping will be able to understand all of the approaches currently used in brain mapping, incorporating multimodality, and multisubject comparisons. Key Features * The only book of its kind * Subject matter is the fastest growing area in the field of brain mapping * Presents geometrically-based approaches to the field of brain mapping * Discusses intensity-based approaches to the field of brain mapping.

Overview: -- A.W. Toga, An Introduction to Brain Warping. -- J. Ashburner and K. Friston, Spatial Normalization. -- Intensity Based Approaches: -- R. Bajcsy, Elastic Deformation Utilizing a Mechanical System. -- S. Kovacic, Multi-Resolution; Multiscale Approaches. -- S. Warfield, A. Robatino, J. Dengler, F. Jolesz, and R. Kikinis, Nonlinear Registration and Template Driven Segmentation. -- G. Christensen, M.I. Miller, and S.C. Joshi, Bayesian Framework for Image Registration Using Eigenfunctions. -- J. Gee, Finite Element Methods. -- M. Miller, S.C. Joshi, and G.E. Christensen, Large Deformation Fluid Diffeomorphisms for Landmark and Image Matching. -- D.L. Collins and A.C. Evans, ANIMAL: Automatic Nonlinear Image Matching and Anatomical Labeling. -- J.-P. Thirion, Diffusing Models and Applications. -- F.L. Bookstein, Linear Methods for Nonlinear Maps. -- H. Mueller and D. Ruprecht, Spatial Interpolants for Warping. -- M.W. Vannier, Global Pattern Matching. -- G. Subsol, Crest-Lines for Curve Based Warping. -- D. Terzopolous, Snakes in Warping and Matching. -- J.H. Downs III, J.L. Lancaster, and P.T. Fox, Surface Based Spatial Normalization Using Convex Hulls. -- S. Lavallee, E. Bittar, and R. Szeliski, Elastic Registration and Inference Using Octree-Splines. -- J.W. Haller, Brain Templates. -- P. Thompson and A.W. Toga, Anatomically-Driven Strategies for High-Dimensional Brain Image Warping and Pathology Detection. -- H. Drury, D.C. Van Essen, M. Corbetta, and A.Z. Snyder, Surface-Based Analyses of the Human Cerebral Cortex. -- R.P. Woods, Automated Global Polynomial Warping. -- Subject Index.

Includes bibliographical references and index.

Overview -- Introduction to brain warping -- Spatial normalization -- Intensity based approaches -- Multiscale/multiresolution representations -- Nonlinear registration and template-driven segmentation -- Bayesian framework for image registration using eigenfunctions -- Numerical methods for high-dimensional warps -- Large deformation fluid diffeomorphisms for landmark and image matching -- ANIMAL: Automatic Nonliner Image Matching and Anatomical Labeling -- Diffusing models and applications -- Linear methods for nonlinear maps: procrustes fits, thin-plate splines, and the biometric analysis of shape variability -- Geometrically based approaches -- Elastic matching: continuum mechanical and probabilistic analysis -- Spatial interpolants for warping -- Global pattern matching -- Crest lines for curve-based warping -- Surface-based spatial normalization using convex hulls -- Elastic registration and inference using oct-tree splines -- Brain templates -- Anatomically driven strategies for high-dimensional brain image warping and pathology detection -- Surface-based analyses of the human cerebral cortex -- Automated global polynomial warping.

Description based on print version record.