[1] Zhao Jing, Luo Guoping, Yao Zhijian, Lu Qing, et al. Depression discrimination using fMRI and DTI databy wavelet based fusion scheme [J]. Journal of Southeast University (English Edition), 2012, 28 (1): 25-28. [doi:10.3969/j.issn.1003-7985.2012.01.005]

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Depression discrimination using fMRI and DTI databy wavelet based fusion scheme()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

28

Issue:

2012 1

Page:

25-28

Research Field:

Biological Science and Medical Engineering

Publishing date:

2012-03-30

Info

Title:

Depression discrimination using fMRI and DTI databy wavelet based fusion scheme

Author(s):

Zhao Jing¹;  Luo Guoping¹;  Yao Zhijian²;  Lu Qing¹

¹ Research Centre for Learning Science, Southeast University, Nanjing 210096, China
² Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China

Keywords:

classification;  functional magnetic resonance imaging(fMRI);  diffusion tensor imaging(DTI);  medical image fusion;  depression

PACS:

Q64;TP310.4

DOI:

10.3969/j.issn.1003-7985.2012.01.005

Abstract:

Both functional magnetic resonance imaging(fMRI)and diffusion tensor imaging(DTI)can provide different information of the human brain, so using the wavelet transform method can achieve a fusion of these two types of image data and can effectively improve the depression recognition accuracy. Multi-resolution wavelet decomposition is used to transform each type of images to the frequency domain in order to obtain the frequency components of the images. To each subject, decomposition components of two images are then added up separately according to their frequencies. The inverse discrete wavelet transform is used to reconstruct the fused images. After that, principal component analysis(PCA)is applied to reduce the dimension and obtain the features of the fusion data before classification. Based on the features of the fused images, an accuracy rate of 80.95% for depression recognition is achieved using a leave-one-out cross-validation test. It can be concluded that this wavelet fusion scheme has the ability to improve the current diagnosis of depression.

References:

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Memo

Memo:

Biographies: Zhao Jing(1987—), male, graduate;Yao Zhijian(corresponding author), male, doctor, associate professor, yaozhijian@yahoo.cn.
Foundation items: The National Natural Science Foundation of China(No.30900356, 81071135).
Citation: Zhao Jing, Luo Guoping, Yao Zhijian, et al. Depression discrimination using fMRI and DTI data by wavelet based fusion scheme[J].Journal of Southeast University(English Edition), 2012, 28(1):25-28.[doi:10.3969/j.issn.1003-7985.2012.01.005]

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