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
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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
Contributors ix(2) Preface xi(2) Acknowledgments xiii SECTION I OVERVIEW 1(44)
1. An Introduction to Brain Warping 1(26) ARTHUR W. TOGA PAUL THOMPSON Overview and Preliminaries 1(2) Origins: A Historical Perspective 3(1) Warping Strategies 4(2) Applications 6(10) Visualization 16(1) Multidimensional Warping 17(5) Summary 22(1) References 22(5)
2. Spatial Normalization 27(18) JOHN ASHBURNER KARL J. FRISTON Background 27(2) Theory 29(1) Mathematics 30(3) Implementation 33(6) Evaluation and Limitations 39(4) Future Directions 43(1) Conclusions 44(1) References 44(1) SECTION II INTENSITY BASED APPROACHES 45(138)
3. Multiscale/Multiresolution Representations 45(22) STANISLAV KOVACIC RUZENA BAJCSY Introduction 45(2) Multiscale Basics 47(9) Registration in a Multiscale Framework 56(1) Some Multiscale Registration Schemes 57(6) Conclusions 63(1) References 63(4)
4. Nonlinear Registration and Template-Driven Segmentation 67(18) SIMON WARFIELD ANDRE ROBATINO JOACHIM DENGLER FERENC JOLESZ RON KIKINIS Introduction 67(1) Nonlinear Registration Techniques 68(7) Template-Driven Segmentation 75(3) Result 78(5) Discussion 83(1) References 83(2)
5. Bayesian Framework for Image Registration Using Eigenfunctions 85(16) GARY E. CHRISTENSEN Background 85(1) Theory 86(1) Mathematics 87(2) Implementations 89(2) Results 91(4) Assumptions and Limitations 95(3) References 98(3)
6. Numerical Methods for High-Dimensional Warps 101(14) JAMES C. GEE DAVID R. HAYNOR Introduction 101(1) Broits Iterative Solution 101(3) The Finite Element Method 104(6) Experimental Comparison of Methods 110(2) Summary 112(1) References 113(2)
7. Large Deformation Fluid Diffeomorphisms for Landmark and Image Matching 115(18) MICHEAL I. MILLER SARANG C. JOSHI GARY E. CHRISTENSEN Background and History 115(2) Theory for Generating Large Deformation Diffeomorphisms 117(1) Algorithms and Implementation for Large Deformation Diffeomorphisms 118(4) Results and Validation 122(8) References 130(3)
8. ANIMAL: Automatic Nonlinear Image Matching and Anatomical Labeling 133(10) LOUIS COLLINS A.C. EVANS Introduction 133(1) Background 134(1) Methods 135(6) Conclusions 141(1) References 141(2)
9. Diffusing Models and Applications 143(14) JEAN-PHILIPPE THIRION Introduction 143(1) Nonrigid Matching Viewed as an Attraction Problem 143(1) Diffusing Models 144(2) Implementations Derived from the Concept of Diffusing Models 146(1) Experiments 147(2) Applications 149(4) Morphoanalysis 153(1) Conclusion 153(1) References 153(4)
10. Linear Methods for Nonlinear Maps: Procrustes Fits, Thin-Plate Splines, and the Biometric Analysis of Shape Variability 157(26) FRED L. BOOKSTEIN Overview 157(1) Introduction 158(1) Some Geometric-Statistical Theory 159(6) Implementations and Additional Linearizations 165(11) Implications for Other Methodologies 176(2) Concluding Comment: The Centrality of Careful: Linearizations for Medical Image Analysis 178(2) References 180(3) SECTION III GEOMETRICALLY BASED APPROACHES 183(194)
11. Elastic Matching: Continuum Mechanical and Probabilistic Analysis 183(16) JAMES C. GEE RUZENA K. BAJCSY Introduction 183(1) Anatomy Atlas 184(1) Elastic Matching 184(2) Bayesian Generalization 186(6) Applications 192(4) Summary 196(1) References 196(3)
12. Spatial Interpolants for Warping 199(22) HEINRICH MULLER DETLEF RUPRECHT Warping with Scattered Data Interpolation Methods 199(1) Distance-Weighted Methods 200(3) Radial Basis Functions 203(2) Simplex-Based Methods 205(1) Natural Neighbour Interpolation 206(1) Extended Data 207(5) Comparison of the Methods 212(1) Warping of Shapes Described by Formulas and Meshes 213(3) Warping of Raster Data 216(3) References 219(2)
13. Global Pattern Matching 221(20) MICHAEL W. VANNIER Background/History 221(3) Theory 224(3) Mathematics 227(3) Implementation 230(5) Validation/Acceptance/Assumptions/Limitations 235(2) Future Directions 237(1) Conclusions 238(1) References 238(3)
14. Crest Lines for Curve-Based Warping 241(22) GERARD SUBSOL Landmarks of the Cortical Surface 241(3) Crest Lines as Landmarks to Modelize Cortical Surface 244(4) Three-Dimensional Nonrigid Registrations of Crest Lines 248(5) Three-Dimensional Warping Based on Crest Lines 253(5) Conclusion 258(1) References 259(4)
15. Surface-Based Spatial Normalization Using Convex Hulls 263(20) J. HUNTER DOWNS III JACK L. LANCASTER PETER T. FOX Background 263(2) Theory 265(4) Implementations 269(2) Validation and Comparisons 271(9) Future Directions 280(1) Conclusions 281(1) References 281(2)
16. Elastic Registration and Inference Using Oct-Tree Splines 283(14) STEPHANE LAVALLEE ERIC BITTAR RICHARD SZELISKI Introduction 283(1) Previous Work 284(1) Problem Formulation 285(2) Least Squares Minimization 287(1) Fast Distance Computation 288(1) Hierarchical Oct-Tree Spline Deformations 289(2) Experimental Results 291(2) Discussion and Conclusions 293(1) References 294(3)
17. Brain Templates 297(14) JOHN W. HALLER Brain Images and Atlases 297(4) The Concept of Brain Templates 301(2) Theory 303(5) Applications 308(1) Limitations 308(1) References 309(2)
18. Anatomically Driven Strategies for High-Dimensional Brain Image Warping and Pathology Detection 311(26) PAUL THOMPSON ARTHUR W. TOGA Challenges in Three-Dimensional Human Brain Mapping 311(2) Classification of Warping Algorithms 313(9) Cortical Surface Matching 322(4) Pathology Detection 326(4) Applications 330(3) Conclusions 333(1) References 333(4)
19. Surface-Based Analyses of the Human Cerebral Contex 337(28) HEATHER A. DRURY DAVID C. VAN ESSEN MAURIZIO CORBETTA ABRAHAM Z. SNYDER Introduction 337(1) Surface Reconstruction 338(9) A Surface-Based Altas of the Human Cortex 347(4) Visualization and Analysis of Experimental Data 351(2) Compensating for Individual Variability 353(5) Discussion 358(2) References 360(2) Appendix: Available Software and Data 362(3)
20. Automated Global Polynomial Warping 365(12) ROGER P. WOODS Background 365(1) Automated Polynomial Warping Theory 366(2) Mathematics and Implementation 368(3) Validation and Comparison of Spatial Transformation Models 371(3) Future Directions 374(2) Conclusions 376(1) References 376(1) Index 377
Dr. Toga is a Professor of Neurology at the UCLA School of Medicine. He is also Director of the Laboratory of Neuro Imaging, which he founded and developed into one of the largest research neuroimaging centers in the world, and Co-Director of the UCLA Brain Mapping Division. With his diverse team, Dr. Toga has been responsible for a number of breakthroughs in various areas of neuroscience. He has published more than 170 papers, including chapters, textbooks, and reviews, and has received numerous honors and awards in computer science, graphics, and neuroscience. Dr. Toga holds the chairmanship of numerous committees within UCLA, NIH, and a variety of international task forces and is the founder and Co-Editor-in-Chief of NeuroImage, also published by Academic Press.
