國立虎尾科技大學 |

Brain Mapping Methods : = Segmentation, Registration, and Connectivity Analysis.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Brain Mapping Methods :/
Reminder of title:	Segmentation, Registration, and Connectivity Analysis.
Author:	Prasad, Gautam.
Description:	1 online resource (135 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781303078576

Brain Mapping Methods : = Segmentation, Registration, and Connectivity Analysis.
Prasad, Gautam.

Brain Mapping Methods :Segmentation, Registration, and Connectivity Analysis. - 1 online resource (135 pages)

Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2013.

Includes bibliographical references

We present a collection of methods that model and interpret information represented in structural magnetic resonance imaging (MRI) and diffusion MRI images of the living human brain. Our solution to the problem of brain segmentation in structural MRI combines artificial life and deformable models to develop a customizable plan for segmentation realized as cooperative deformable organisms. We also present work to represent and register white matter pathways as described in diffusion MRI. Our method represents these pathways as maximum density paths (MDPs), which compactly represent information and are compared using shape based registration for population studies. In addition, we present a group of methods focused on connectivity in the brain. These include an optimization for a global probabilistic tractography algorithm that computes fibers representing connectivity pathways in tissue, a novel maximum-flow based measure of connectivity, a classification framework identifying Alzheimer's disease based on connectivity measures, and a statistical framework to find the optimal partition of the brain for connectivity analysis. These methods seek to advance our understanding and analysis of neuroimaging data from crucial pre-processing steps to our fundamental understanding of connectivity in the brain.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781303078576Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Brain Mapping Methods : = Segmentation, Registration, and Connectivity Analysis.
LDR:02515ntm a2200325K 4500 001 913703
005 20180622095236.5
006 m o u
007 cr mn||||a|a||
008 190606s2013 xx obm 000 0 eng d
020 $a 9781303078576
035 $a (MiAaPQ)AAI3561080
035 $a (MiAaPQ)ucla:11218
035 $a AAI3561080
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Prasad, Gautam. $3 1186648
245 1 0 $a Brain Mapping Methods : $b Segmentation, Registration, and Connectivity Analysis.
264 0 $c 2013
300 $a 1 online resource (135 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
500 $a Advisers: Demetri Terzopoulos; Paul M. Thompson.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2013.
504 $a Includes bibliographical references
520 $a We present a collection of methods that model and interpret information represented in structural magnetic resonance imaging (MRI) and diffusion MRI images of the living human brain. Our solution to the problem of brain segmentation in structural MRI combines artificial life and deformable models to develop a customizable plan for segmentation realized as cooperative deformable organisms. We also present work to represent and register white matter pathways as described in diffusion MRI. Our method represents these pathways as maximum density paths (MDPs), which compactly represent information and are compared using shape based registration for population studies. In addition, we present a group of methods focused on connectivity in the brain. These include an optimization for a global probabilistic tractography algorithm that computes fibers representing connectivity pathways in tissue, a novel maximum-flow based measure of connectivity, a classification framework identifying Alzheimer's disease based on connectivity measures, and a statistical framework to find the optimal partition of the brain for connectivity analysis. These methods seek to advance our understanding and analysis of neuroimaging data from crucial pre-processing steps to our fundamental understanding of connectivity in the brain.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Neurosciences. $3 593561
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0317
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Los Angeles. $b Computer Science 0201. $3 1182286
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3561080 $z click for full text (PQDT)