29 November 2017 Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels
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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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Leyuan Fang, Leyuan Fang, Chong Wang, Chong Wang, Shutao Li, Shutao Li, Jun Yan, Jun Yan, Xiangdong Chen, Xiangdong Chen, Hossein Rabbani, Hossein Rabbani, } "Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels," Journal of Biomedical Optics 22(11), 116011 (29 November 2017). https://doi.org/10.1117/1.JBO.22.11.116011 . Submission: Received: 10 June 2017; Accepted: 8 November 2017
Received: 10 June 2017; Accepted: 8 November 2017; Published: 29 November 2017
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