An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image

Abstract

(Background and objectives) Retinal artery and vein classification is an important task for the automatic computer-aided diagnosis of various eye diseases and systemic diseases. This paper presents an improved supervised artery and vein classification method in retinal image. (Methods) Intra-image regularization and inter-subject normalization is applied to reduce the differences in feature space. Novel features, including first-order and second-order texture features, are utilized to capture the discriminating characteristics of arteries and veins. (Results) The proposed method was tested on the DRIVE dataset and achieved an overall accuracy of 0.923. (Conclusion) This retinal artery and vein classification algorithm serves as a potentially important tool for the early diagnosis of various diseases, including diabetic retinopathy and cardiovascular diseases.