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Age-related Macular Degeneration Identification In Volumetric Optical Coherence Tomography Using Decomposition and Local Feature Extraction

In this paper we proposed a decomposition based approach, coupled with local feature extraction, to support the analysis of Three-Dimensional (3D) Optical Coherence Tomography (OCT) images so as to determine the presence (or otherwise) of Age-related Macular Degeneration (AMD) in the retina of the human eye. AMD is one of the leading causes of vision loss in people aged over 50 years in the world. The 3D OCT imaging technique has become an indispensable diagnostic tool for the management of AMD. However, there is a lack of automated decision-making tools for analysing the large volumes of data that can be collected using OCT. In order to address this problem, a volumetric analysis technique is proposed for the automated diagnosis of AMD in 3D OCT images without the need for detecting AMD lesions. The process commences with the decomposition of a given image into sub-regions by recursively dividing a volume into sub-volumes. Then, for each sub-volume, oriented gradient local binary pattern histograms are extracted and formed into a feature vector to which classifier generation techniques can be applied. The proposed technique was evaluated using ten-fold cross validation by applying it to 140 volumetric OCT images, the results demonstrated a promising performance with a best Area Under the receiver operating Curve (AUC) value of 94.4%.