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Chung M.K. Statistical and Computational Methods in Brain Image Analysis

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Chung M.K. Statistical and Computational Methods in Brain Image Analysis
CRC Press, 2013. — 432 p.
Brain image analysis is an emerging new field that utilizes various non-invasive brain imaging modalities such as MRI, fMRI, PET and DTI in mapping out the 4D spatiotemporal dynamics of the human brain in both normal and clinical populations in macroscopic level. This discipline emerged about twenty years ago and has made substantial progress in the past two decades. A major challenge in the field is caused by the massive amount of nonstandard high dimensional imaging data that is difficult to analyze using available standard techniques. This requires new computational approaches and solutions. The main goals of this book are to provide an overview of various statistical and computational methodologies used in the field to a wide range of researchers and students, and to articulate important yet technically challenging topics further. The book is mainly focused on methodological issues in analyzing structural brain imaging modalities such as MRI and DTI. Concepts and methods are illustrated with real imaging applications and examples. Most of the brain imaging data set along with MATLABR_ codes used in the book can be downloaded from http://brainimaging.waisman.wisc.edu/~chung/BIA.
Although there are abundant research papers scattered in various journals, there is not a single research paper or book that covers many quantitative techniques used in the field with detailed illustrations of actual imaging data and computer codes. By making the data and codes available, we tried to make the book more accessible to a wide range of researchers and students.
We wish to provide methodological understanding in a manner immediately usable to researchers who actually need it. The book has many examples and case studies, and is structured mainly as a graduate level textbook. The key features of this book are as follows.
The book presents the coherent statistical and mathematical treatment of underlying methods. Only the methods that have been found to be useful in neuroimaging applications are presented.
Introduction to the real examples and case studies that have been previously published in journals. Most of the brain imaging data and codes illustrated in the textbook can be downloaded from the website.
We put a significant emphasis on image visualization using MATLAB. We have provided many publication quality visualization examples that have been actually used in publishing journal papers.
Introduction to Brain and Medical Images
Bernoulli Models for Binary Images
General Linear Models
Gaussian Kernel Smoothing
Random Fields Theory
Anisotropic Kernel Smoothing
Multivariate General Linear Models
Cortical Surface Analysis
Heat Kernel Smoothing on Surfaces
Cosine Series Representation of 3D Curves
Weighted Spherical Harmonic Representation
Multivariate Surface Shape Analysis
Laplace-Beltrami Eigenfunctions for Surface Data
Persistent Homology
Sparse Networks
Sparse Shape Models
Modeling Structural Brain Networks
Mixed Effects Models
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