Texture classification of retinal layers in optical coherence tomography

Abstract

This work investigates the ability of texture analysis to yield discrimination of retinal tissue layers in the images provided by Optical Coherence Tomography (OCT). In fact, this relatively new imaging technology allows noninvasive visualization of retinal layers. Their segmentation is a prerequisite for any computer method that aims to objectively extract valuable information, regarding the condition and the progression of disease and therapy. Since the regularities of biological tissue can be captured by texture analysis in a straightforward way, a computer approach is proposed based on co-occurence matrices and artificial neural networks (ANN) for the classification and analysis of single retinal layers. A subset of ten normal eyes has been used for the training phase, and another subset of ten normal eyes has been used for testing the system performance. For inner retinal layers, accuracy was 79%, specificity about 71% and sensibility was 87%. Slightly lower values were obtained for outer retinal layers. These preliminary results suggest that this approach may be useful as a prototype system for a quantitative characterization of retinal tissue.