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Journal of Biomedical Optics • new

Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels
Author(s): Leyuan Fang; Chong Wang; Shutao Li; Jun Yan; Xiangdong Chen; Hossein Rabbani
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Paper Abstract

We present an automatic method, termed as the principal component analysis network with composite kernel (PCANet-CK), for the classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images. Specifically, the proposed PCANet-CK method first utilizes the PCANet to automatically learn features from each B-scan of the 3-D retinal OCT images. Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images. Finally, the fused (composite) kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT (SD-OCT) datasets (of normal subjects and subjects with the macular edema and age-related macular degeneration), which demonstrated its effectiveness.

Paper Details

Date Published: 29 November 2017
PDF: 10 pages
J. Biomed. Opt. 22(11) 116011 doi: 10.1117/1.JBO.22.11.116011
Published in: Journal of Biomedical Optics Volume 22, Issue 11
Show Author Affiliations
Leyuan Fang, Hunan Univ. (China)
Chong Wang, Hunan Univ. (China)
Shutao Li, Hunan Univ. (China)
Jun Yan, Hunan Univ. (China)
Xiangdong Chen, Hunan Univ. of Chinese Medicine (China)
Hossein Rabbani, Isfahan Univ. of Medical Sciences (Iran)


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