Classification for Alzheimer's disease from structural MRI by general n-dimensional principal component analysis

Song Yuan Tang

doi:10.4028/www.scientific.net/AMM.336-338.2316

Classification for Alzheimer's disease from structural MRI by general n-dimensional principal component analysis

Song Yuan Tang

School of Optics and Photonics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, we propose a classification method for Alzheimer's disease from structural MRI. In the method, a specific template is firstly constructed. Then all data are registered to the template and the corresponding Jacobians are calculated. And then, a general n-dimensional principal component analysis (GND-PCA) based method is adopted to extract features from the Jacobians and the features are enhanced by the linear discriminant analysis (LDA). Finally, the enhanced features are used for the support vector machines (SVMs) classifiers. The proposed method classifies AD and normal controls (NC) well.

Original language	English
Title of host publication	Industrial Instrumentation and Control Systems II
Pages	2316-2319
Number of pages	4
DOIs	https://doi.org/10.4028/www.scientific.net/AMM.336-338.2316
Publication status	Published - 2013
Event	2013 2nd International Conference on Measurement, Instrumentation and Automation, ICMIA 2013 - Guilin, China Duration: 23 Apr 2013 → 24 Apr 2013

Publication series

Name	Applied Mechanics and Materials
Volume	336-338
ISSN (Print)	1660-9336
ISSN (Electronic)	1662-7482

Conference

Conference	2013 2nd International Conference on Measurement, Instrumentation and Automation, ICMIA 2013
Country/Territory	China
City	Guilin
Period	23/04/13 → 24/04/13

Keywords

Alzheimer's disease
Generalized N-dimensional principal component analysis (GND-PCA)
Magnetic resonance imaging (MRI)
Support vector machines (SVMs)

Access to Document

10.4028/www.scientific.net/AMM.336-338.2316

Cite this

@inproceedings{43b47d091abe49debf23792edd790f97,

title = "Classification for Alzheimer's disease from structural MRI by general n-dimensional principal component analysis",

abstract = "In this paper, we propose a classification method for Alzheimer's disease from structural MRI. In the method, a specific template is firstly constructed. Then all data are registered to the template and the corresponding Jacobians are calculated. And then, a general n-dimensional principal component analysis (GND-PCA) based method is adopted to extract features from the Jacobians and the features are enhanced by the linear discriminant analysis (LDA). Finally, the enhanced features are used for the support vector machines (SVMs) classifiers. The proposed method classifies AD and normal controls (NC) well.",

keywords = "Alzheimer's disease, Generalized N-dimensional principal component analysis (GND-PCA), Magnetic resonance imaging (MRI), Support vector machines (SVMs)",

author = "Tang, {Song Yuan}",

year = "2013",

doi = "10.4028/www.scientific.net/AMM.336-338.2316",

language = "English",

isbn = "9783037857519",

series = "Applied Mechanics and Materials",

pages = "2316--2319",

booktitle = "Industrial Instrumentation and Control Systems II",

note = "2013 2nd International Conference on Measurement, Instrumentation and Automation, ICMIA 2013 ; Conference date: 23-04-2013 Through 24-04-2013",

}

Tang, SY 2013, Classification for Alzheimer's disease from structural MRI by general n-dimensional principal component analysis. in Industrial Instrumentation and Control Systems II. Applied Mechanics and Materials, vol. 336-338, pp. 2316-2319, 2013 2nd International Conference on Measurement, Instrumentation and Automation, ICMIA 2013, Guilin, China, 23/04/13. https://doi.org/10.4028/www.scientific.net/AMM.336-338.2316

TY - GEN

T1 - Classification for Alzheimer's disease from structural MRI by general n-dimensional principal component analysis

AU - Tang, Song Yuan

PY - 2013

Y1 - 2013

N2 - In this paper, we propose a classification method for Alzheimer's disease from structural MRI. In the method, a specific template is firstly constructed. Then all data are registered to the template and the corresponding Jacobians are calculated. And then, a general n-dimensional principal component analysis (GND-PCA) based method is adopted to extract features from the Jacobians and the features are enhanced by the linear discriminant analysis (LDA). Finally, the enhanced features are used for the support vector machines (SVMs) classifiers. The proposed method classifies AD and normal controls (NC) well.

AB - In this paper, we propose a classification method for Alzheimer's disease from structural MRI. In the method, a specific template is firstly constructed. Then all data are registered to the template and the corresponding Jacobians are calculated. And then, a general n-dimensional principal component analysis (GND-PCA) based method is adopted to extract features from the Jacobians and the features are enhanced by the linear discriminant analysis (LDA). Finally, the enhanced features are used for the support vector machines (SVMs) classifiers. The proposed method classifies AD and normal controls (NC) well.

KW - Alzheimer's disease

KW - Generalized N-dimensional principal component analysis (GND-PCA)

KW - Magnetic resonance imaging (MRI)

KW - Support vector machines (SVMs)

UR - http://www.scopus.com/inward/record.url?scp=84883038629&partnerID=8YFLogxK

U2 - 10.4028/www.scientific.net/AMM.336-338.2316

DO - 10.4028/www.scientific.net/AMM.336-338.2316

M3 - Conference contribution

AN - SCOPUS:84883038629

SN - 9783037857519

T3 - Applied Mechanics and Materials

SP - 2316

EP - 2319

BT - Industrial Instrumentation and Control Systems II

T2 - 2013 2nd International Conference on Measurement, Instrumentation and Automation, ICMIA 2013

Y2 - 23 April 2013 through 24 April 2013

ER -

Classification for Alzheimer's disease from structural MRI by general n-dimensional principal component analysis

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this