An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image. (April 2017)

• Record Type: Journal Article
• Authors: Xu, Xiayu; Ding, Wenxiang; Abràmoff, Michael D.; Cao, Ruofan
• Is Part Of: Computer methods and programs in biomedicine. Volume 141(2017)
• Page Start: 3
• Page End: 9
• Publication Date: 2017-04
• Subjects: Retinal image -- Arteriovenous classification -- Image analysis -- Computer-aided diagnostics; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.01.007
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This paper proposed an improved pixel-classification based method for artery and vein classification on retinal image. Intra-image regularization and inter-subject normalization are first used to reduce the image differences in feature space. Novel features, including first-order and second-order texture features, are introduced to capture the discriminating characteristics. A high accuracy of 0.923 was achieved on a public database. The proposed method holds great potential to serve as an early diagnostic tool for various diseases, such as diabetic retinopathy. 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.