Statistical and Computational Methods in Brain Image Analysis

Statistical and Computational Methods in Brain Image Analysis (Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series)

The massive amount of nonstandard high-dimensional brain imaging data being generated is often difficult to analyze using current techniques. This challenge in brain image analysis requires new computational approaches and solutions. But none of the research papers or books in the field describe the quantitative techniques with detailed illustrations of actual imaging data and computer codes. Using MATLAB^® and case study data sets, Statistical and Computational Methods in Brain Image Analysis is the first book to explicitly explain how to perform statistical analysis on brain imaging data.

The book focuses on methodological issues in analyzing structural brain imaging modalities such as MRI and DTI. Real imaging applications and examples elucidate the concepts and methods. In addition, most of the brain imaging data sets and MATLAB codes are available on the author’s website.

By supplying the data and codes, this book enables researchers to start their statistical analyses immediately. Also suitable for graduate students, it provides an understanding of the various statistical and computational methodologies used in the field as well as important and technically challenging topics.

Table of Contents

Chapter 1: Introduction to Brain and Medical Images
Chapter 2: Bernoulli Models for Binary Images
Chapter 3: General Linear Models
Chapter 4: Gaussian Kernel Smoothing
Chapter 5: Random Fields Theory
Chapter 6: Anisotropic Kernel Smoothing
Chapter 7: Multivariate General Linear Models
Chapter 8: Cortical Surface Analysis
Chapter 9: Heat Kernel Smoothing on Surfaces
Chapter 10: Cosine Series Representation of 3D Curves
Chapter 11: Weighted Spherical Harmonic Representation
Chapter 12: Multivariate Surface Shape Analysis
Chapter 13: Laplace-Beltrami Eigenfunctions for Surface Data
Chapter 14: Persistent Homology
Chapter 15: Sparse Networks
Chapter 16: Sparse Shape Models
Chapter 17: Modeling Structural Brain Networks
Chapter 18: Mixed Effects Models