| Description |
1 online resource (xv, 403 pages) : illustrations (some color) |
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text file |
| Bibliography |
Includes bibliographical references (pages 367-398) and index. |
| Contents |
Preface; Contents; 1. Statistical Preliminary; 1.1 General Linear Models; 1.2 Random Fields; 1.2.1 Covariance Functions; 1.2.2 Gaussian Random Fields; 1.2.3 Differentiation and Integration of Fields; 1.2.4 Statistical Inference on Fields; 1.3 Multiple Comparisons; 1.3.1 Bonferroni Correction; 1.3.2 Random Fields Theory; 1.3.3 Poisson Clumping Heuristic; 1.3.4 Euler Characteristic Method; 1.3.5 Intrinsic Volume; 1.3.6 Euler Characteristic Density; 1.4 Statistical Power Analysis; 1.4.1 Statistical Power at a Voxel; 1.4.2 Statistical Power under Multiple Comparisons. |
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2. Deformation-Based Morphometry2.1 Image Registration; 2.2 Deformation-Based Morphometry; 2.3 Displacement Vector Fields; 2.3.1 Dynamic Model on Displacement; 2.3.2 Local Inference via Hotelling's T2-Field; 2.3.3 Detecting Local Brain Growth; 2.4 Global Inference via Integral Statistic; 2.4.1 Karhunen-Lo eve Expansion; 2.4.2 Mercer's Theorem; 2.4.3 Integral Statistic on Displacement; 3. Tensor-Based Morphometry; 3.1 Jacobian Determinant; 3.2 Distributional Assumptions; 3.3 Local Volume Changes; 3.4 Longitudinal Modeling; 3.4.1 Normal Brain Development in Children. |
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3.5 Global Inference via Divergence Theorem3.6 Second Order Tensor Fields; 3.6.1 Membrane Spline Energy; 3.6.2 Vorticity Tensor Fields; 3.6.3 Generalized Variance Field; 4. Voxel-Based Morphometry; 4.1 Image Segmentation; 4.1.1 Mumford-Shah Model; 4.1.2 Level Sets; 4.1.3 Active Contours; 4.1.4 Deformable Surface Models; 4.1.5 Thin-Plate Spline Thresholding; 4.2 Mixture Models; 4.2.1 Bayesian Segmentation; 4.2.2 Mixture Models; 4.2.3 Expectation Maximization Algorithm; 4.2.4 Two Components Gaussian Mixtures; 4.3 Voxel-Based Morphometry; 4.3.1 ROI Volume Estimation in VBM. |
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4.3.2 Limitations of Witelson Partition4.3.3 General Linear Models on Tissue Densities; 4.3.4 2D VBM Applied to Corpus Callosum; 5. Geometry of Cortical Manifolds; 5.1 Surface Parameterization; 5.1.1 B-Spline Parameterization; 5.1.2 B-Spline Curves; 5.1.3 Quadratic Parameterization; 5.1.4 Fourier Descriptors; 5.2 Surface Normals and Curvatures; 5.2.1 Surface Normals; 5.2.2 Gaussian and Mean Curvatures; 5.2.3 Curvatures of Polynomial Surfaces; 5.3 Laplace-Beltrami Operator; 5.3.1 Eigenfunctions of Laplace-Beltrami Operator; 5.3.2 Multiplicity of Eigenfunctions. |
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5.3.3 Laplace-Beltrami Shape Descriptors5.3.4 Second Eigenfunctions; 5.3.5 Dirichlet Energy; 5.3.6 Fiedler's Vector; 5.4 Finite Element Methods; 5.4.1 Pieacewise Linear Functions; 5.4.2 Mass and Stiffness Matrices; 6. Smoothing on Cortical Manifolds; 6.1 Gaussian Kernel Smoothing; 6.1.1 Isotropic Gaussian Kernel; 6.1.2 Anisotropic Gaussian Kernel; 6.2 Diffusion Smoothing; 6.2.1 Diffusion in Euclidean Space; 6.2.2 Diffusion in 1D; 6.2.3 Diffusion on Triangular Mesh; 6.2.4 Finite Difference Scheme; 6.3 Heat Kernel Smoothing; 6.3.1 Heat Kernel; 6.3.2 Heat Kernel Smoothing. |
| Note |
6.3.3 Iterated Kernel Smoothing. |
| Summary |
Computational neuroanatomy is an emerging field that utilizes various non-invasive brain imaging modalities, such as MRI and DTI, in quantifying the spatiotemporal dynamics of the human brain structures in both normal and clinical populations. This discip. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Neuroanatomy -- Mathematics.
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Neuroanatomy. |
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Mathematics. |
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Neuroanatomy -- Statistical methods.
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Statistics. |
| Genre/Form |
Electronic books.
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Electronic books -- Electronic books.
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| Other Form: |
Print version: Chung, Moo K. Computational neuroanatomy. Singapore ; New Jersey : World Scientific, ©2013 9789814335430 (OCoLC)819383781 |
| ISBN |
9789814335447 (electronic book) |
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9814335444 (electronic book) |
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9814335436 (hardback) |
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9789814335430 (hardback) |
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9781299133068 (MyiLibrary) |
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1299133061 (MyiLibrary) |
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9789814335430 |
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