LEADER 04016nam 2200637 a 450 001 9910815748203321 005 20200520144314.0 010 $a0-429-09432-9 010 $a1-4398-3636-1 024 7 $a10.1201/b15056 035 $a(CKB)2670000000387709 035 $a(EBL)1222354 035 $a(SSID)ssj0000890309 035 $a(PQKBManifestationID)11478782 035 $a(PQKBTitleCode)TC0000890309 035 $a(PQKBWorkID)10883333 035 $a(PQKB)10737537 035 $a(OCoLC)851696122 035 $a(MiAaPQ)EBC1222354 035 $a(PPN)183348109 035 $a(EXLCZ)992670000000387709 100 $a20130530d2014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aStatistical and computational methods in brain image analysis /$fMoo K. Chung 205 $a1st ed. 210 $aBoca Raton $cCRC Press$d2014 215 $a1 online resource (432 p.) 225 1 $aChapman & Hall/CRC mathematical and computational imaging sciences series 300 $a"A Chapman & Hall book." 311 $a1-299-71057-3 311 $a1-4398-3635-3 320 $aIncludes bibliographical references. 327 $aFront 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 Analysis 327 $aChapter 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 Cover 330 $a"The massive amount of nonstandard high-dimensional brain imaging data being generated is often difficult to analyze using current techniques. This challenge in brain image analysis requires new computational approaches and solutions. But none of the research papers or books in the field describe the quantitative techniques with detailed illustrations of actual imaging data and computer codes. Using MATLAB and case study data sets, Statistical and Computational Methods in Brain Image Analysis is the first book to explicitly explain how to perform statistical analysis on brain imaging data. The book focuses on methodological issues in analyzing structural brain imaging modalities such as MRI and DTI. Real imaging applications and examples elucidate the concepts and methods. In addition, most of the brain imaging data sets and MATLAB codes are available on the author's website. By supplying the data and codes, this book enables researchers to start their statistical analyses immediately. Also suitable for graduate students, it provides an understanding of the various statistical and computational methodologies used in the field as well as important and technically challenging topics."--$cProvided by publisher. 410 0$aChapman & Hall/CRC mathematical and computational imaging sciences. 606 $aBrain$xImaging 606 $aBrain$xImaging$xStatistical methods 606 $aBrain mapping$xStatistical methods 615 0$aBrain$xImaging. 615 0$aBrain$xImaging$xStatistical methods. 615 0$aBrain mapping$xStatistical methods. 676 $a616.8/04754 686 $aMAT029000$aSCI089000$aTEC059000$2bisacsh 701 $aChung$b Moo K$01198179 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910815748203321 996 $aStatistical and computational methods in brain image analysis$94059432 997 $aUNINA