An Automated Age-Related Macular Degeneration Classification Based on Local Texture Features in Optical Coherence Tomography Angiography

Abstract

In this paper, an age-related macular degeneration (AMD) classification algorithm based on local texture features is proposed to support the automated analysis of optical coherence tomography angiography (OCTA) images in wet AMD. The algorithm is based on rotation invariant uniform Local Binary Patterns ($LBPs^{riu2}$) as a texture measurement technique. It was chosen due to its computational simplicity and its invariance against any transformation of the grey level as well as against texture orientation change. The texture features are extracted from the whole image without targeting a particular area. The algorithm was tested on two-dimensional angiogram greyscale images of four different retinal layers acquired via OCTA scan. The evaluation was performed using a ten-fold cross-validation strategy applied to a set of 184 OCTA images consisting of 92 normal control and 92 wet AMD images. The classification was performed on each separate retinal layer, and on all layers together. According to the results, the algorithm was able to achieve a promising performance with mean accuracy of 89% for all layers together and 89%, 94%, 98% and 100% for the superficial, deep, outer and choriocapillaris layers respectively.