Automatic classification of retinal three dimensional optical coherence tomography images using principal component analysis network with composite kernels
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.
Journal of biomedical optics