SVM-based texture classification in optical coherence tomography

Abstract

This paper describes a new method for automated textureclassification for glaucoma detection using high resolutionretinal Optical Coherence Tomography (OCT). OCT is anon-invasive technique that produces cross-sectional imageryof ocular tissue. Here, we exploit information from OCT images,specifically the inner retinal layer thickness and specklepatterns, to detect glaucoma. The proposed method relies onsupport vector machines (SVM), while principal componentanalysis (PCA) is also employed to improve classificationperformance. Results show that texture features can improveclassification accuracy over what is achieved using only layerthickness as existing methods currently do