Read more
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. Unfortunately, few research papers or books in the field describe the quantitative techniques with detailed illus
List of contents
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. Bibliography. Index.
About the author
Moo K. Chung, Ph.D. is an associate professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison. He is also affiliated with the Waisman Laboratory for Brain Imaging and Behavior. He has won the Vilas Associate Award for his applied topological research (persistent homology) to medical imaging and the Editor's Award for best paper published in
Journal of Speech, Language, and Hearing Research. Dr. Chung received a Ph.D. in statistics from McGill University. His main research area is computational neuroanatomy, concentrating on the methodological development required for quantifying and contrasting anatomical shape variations in both normal and clinical populations at the macroscopic level using various mathematical, statistical, and computational techniques.
Summary
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. Unfortunately, few research papers or books in the field describe the quantitative techniques with detailed illus
