Combined SVM and PCA to Recognize the Brain Function from fMRI Images

Author

Guo Rong ; Xie Song-yun ; Cheng Xi-na ; Zhao Hai-tao

Author_Institution

Dept. of Electron. & Inf., Northwestern Polytech. Univ., Xi´an, China

fYear

2009

fDate

11-13 June 2009

Firstpage

1

Lastpage

3

Abstract

In this paper, SVM and PCA are incorporated to classify brain fMRI images. This method well overcomes the difficulty of classifying high-dimensional data. PCA is utilized to extract the most representative features. SVM classifier based on selected features is trained to decode brain states. Experimental results show that the proposed method yields good performance. The correct classification rate of our bi-class recognition problems reaches as high as 97%.

Keywords

biomedical MRI; brain; feature extraction; image classification; medical image processing; neurophysiology; principal component analysis; support vector machines; PCA; SVM classifier; bi-class recognition; brain function recognition; brain state decoding; fMRI image; feature extraction; functional magnetic resonance imaging; high-dimensional data classification; principal component analysis; support vector machine; Biomedical imaging; Data mining; Decoding; Feature extraction; Hospitals; Image recognition; Magnetic resonance imaging; Principal component analysis; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-2901-1

Electronic_ISBN

978-1-4244-2902-8

Type

conf

DOI

10.1109/ICBBE.2009.5162526

Filename

5162526