01104nam0-22003491i-450-99000787729040332120040430085534.084-7800-801-2000787729FED01000787729(Aleph)000787729FED0100078772920040430d2002----km-y0itay50------baspaESa-------001yyDe libros, librerias, imprentas y lectoresdirigido por Pedro M. Catedra y Maria Luisa Lopez-Vidrieroedición al cuidado de Pablo Andrés EscapaSalamancaEdiciones Universidad de Salamanca2002537 p.ill.25 cm<<El>> libro antiguo español6Libri antichiSpagna002.0946Cátedra,Pedro ManuelLopez-Vidriero,Maria LuisaEscapa,Pablo AndrésITUNINARICAUNIMARCBK990007877290403321P.3 B 14128FLFBCFLFBCDe libros, librerias, imprentas y lectores672572UNINA03993oam 2200625I 450 991078682580332120170918194243.00-429-09432-91-4398-3636-110.1201/b15056 (CKB)2670000000387709(EBL)1222354(SSID)ssj0000890309(PQKBManifestationID)11478782(PQKBTitleCode)TC0000890309(PQKBWorkID)10883333(PQKB)10737537(OCoLC)851696122(MiAaPQ)EBC1222354(PPN)183348109(EXLCZ)99267000000038770920180331d2014 uy 0engur|n|---|||||txtccrStatistical and computational methods in brain image analysis /Moo K. ChungBoca Raton :CRC Press,2014.1 online resource (432 p.)Chapman & Hall/CRC mathematical and computational imaging sciences series"A Chapman & Hall book."1-299-71057-3 1-4398-3635-3 Includes bibliographical references.Front Cover; Contents; Preface; 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 AnalysisChapter 13: Laplace-Beltrami Eigenfunctions for Surface DataChapter 14: Persistent Homology; Chapter 15: Sparse Networks; Chapter 16: Sparse Shape Models; Chapter 17: Modeling Structural Brain Networks; Chapter 18: Mixed Effects Models; Bibliography; Color Insert; Back CoverThe 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.--Provided by publisher.Chapman & Hall/CRC mathematical and computational imaging sciences.BrainImagingBrainImagingStatistical methodsBrain mappingStatistical methodsBrainImaging.BrainImagingStatistical methods.Brain mappingStatistical methods.612.82MAT029000SCI089000TEC059000bisacshChung Moo K.1198179MiAaPQMiAaPQMiAaPQBOOK9910786825803321Statistical and computational methods in brain image analysis3741381UNINA