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Automatic Classification of Retinal Three-Dimensional Optical Coherence Tomography Images Using Principal Component Analysis Network With Composite Kernels Publisher Pubmed



Fang L1 ; Wang C1 ; Li S1 ; Yan J1 ; Chen X2 ; Rabbani H3
Authors
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Authors Affiliations
  1. 1. Hunan University, College of Electrical and Information Engineering, Changsha, China
  2. 2. First Affiliated Hospital of Hunan University of Chinese Medicine, Department of Ophthalmology, Changsha, China
  3. 3. Isfahan University of Medical Sciences, Medical Image and Signal Processing Research Center, Isfahan, Iran

Source: Journal of Biomedical Optics Published:2017


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).
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