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

(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. (C) 2017 Elsevier B.V. All rights reserved.