Pattern based norphometry

Bilwaj Gaonkar; Kilian Pohl; Christos Davatzikos

doi:10.1007/978-3-642-23629-7_56

Pattern based norphometry

Bilwaj Gaonkar, Kilian Pohl, Christos Davatzikos

Research output: Contribution to journal › Conference article › peer-review

9 Scopus citations

Abstract

Voxel based morphometry (VBM) is widely used in the neuroimaging community to infer group differences in brain morphology. VBM is effective in quantifying group differences highly localized in space. However it is not equally effective when group differences might be based on interactions between multiple brain networks. We address this by proposing a new framework called pattern based morphometry (PBM). PBM is a data driven technique. It uses a dictionary learning algorithm to extract global patterns that characterize group differences. We test this approach on simulated and real data obtained from ADNI . In both cases PBM is able to uncover complex global patterns effectively.

Original language	English (US)
Pages (from-to)	459-466
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6892 LNCS
Issue number	PART 2
DOIs	https://doi.org/10.1007/978-3-642-23629-7_56
State	Published - 2011
Externally published	Yes
Event	14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada Duration: Sep 18 2011 → Sep 22 2011

Keywords

machine learning
pattern based morphometry
voxel based morphometry

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-23629-7_56

Cite this

@article{9352211abdec41b9acedeb01964c5168,

title = "Pattern based norphometry",

abstract = "Voxel based morphometry (VBM) is widely used in the neuroimaging community to infer group differences in brain morphology. VBM is effective in quantifying group differences highly localized in space. However it is not equally effective when group differences might be based on interactions between multiple brain networks. We address this by proposing a new framework called pattern based morphometry (PBM). PBM is a data driven technique. It uses a dictionary learning algorithm to extract global patterns that characterize group differences. We test this approach on simulated and real data obtained from ADNI . In both cases PBM is able to uncover complex global patterns effectively.",

keywords = "machine learning, pattern based morphometry, voxel based morphometry",

author = "Bilwaj Gaonkar and Kilian Pohl and Christos Davatzikos",

year = "2011",

doi = "10.1007/978-3-642-23629-7_56",

language = "English (US)",

volume = "6892 LNCS",

pages = "459--466",

journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

issn = "0302-9743",

publisher = "Springer Verlag",

number = "PART 2",

note = "14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 ; Conference date: 18-09-2011 Through 22-09-2011",

}

TY - JOUR

T1 - Pattern based norphometry

AU - Gaonkar, Bilwaj

AU - Pohl, Kilian

AU - Davatzikos, Christos

PY - 2011

Y1 - 2011

N2 - Voxel based morphometry (VBM) is widely used in the neuroimaging community to infer group differences in brain morphology. VBM is effective in quantifying group differences highly localized in space. However it is not equally effective when group differences might be based on interactions between multiple brain networks. We address this by proposing a new framework called pattern based morphometry (PBM). PBM is a data driven technique. It uses a dictionary learning algorithm to extract global patterns that characterize group differences. We test this approach on simulated and real data obtained from ADNI . In both cases PBM is able to uncover complex global patterns effectively.

AB - Voxel based morphometry (VBM) is widely used in the neuroimaging community to infer group differences in brain morphology. VBM is effective in quantifying group differences highly localized in space. However it is not equally effective when group differences might be based on interactions between multiple brain networks. We address this by proposing a new framework called pattern based morphometry (PBM). PBM is a data driven technique. It uses a dictionary learning algorithm to extract global patterns that characterize group differences. We test this approach on simulated and real data obtained from ADNI . In both cases PBM is able to uncover complex global patterns effectively.

KW - machine learning

KW - pattern based morphometry

KW - voxel based morphometry

UR - http://www.scopus.com/inward/record.url?scp=82255183599&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=82255183599&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-23629-7_56

DO - 10.1007/978-3-642-23629-7_56

M3 - Conference article

C2 - 21995061

AN - SCOPUS:82255183599

SN - 0302-9743

VL - 6892 LNCS

SP - 459

EP - 466

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

IS - PART 2

T2 - 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011

Y2 - 18 September 2011 through 22 September 2011

ER -

Pattern based norphometry

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this