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

Xiayu Xu; Wenxiang Ding; Michael D Abràmoff; Ruofan Cao

doi:10.1016/j.cmpb.2017.01.007

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

Comput Methods Programs Biomed. 2017 Apr:141:3-9. doi: 10.1016/j.cmpb.2017.01.007. Epub 2017 Jan 17.

Authors

Xiayu Xu¹, Wenxiang Ding², Michael D Abràmoff³, Ruofan Cao⁴

Affiliations

¹ The Key Laboratory of Biomedical Information Engineering, Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China. Electronic address: xiayuxu@xjtu.edu.cn.
² The Key Laboratory of Biomedical Information Engineering, Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China.
³ Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA 52242, USA.
⁴ The Key Laboratory of Biomedical Information Engineering, Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China. Electronic address: ruofanc@mail.xjtu.edu.cn.

PMID: 28241966
DOI: 10.1016/j.cmpb.2017.01.007

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.

Keywords: Arteriovenous classification; Computer-aided diagnostics; Image analysis; Retinal image.

MeSH terms

Early Diagnosis
Humans
Retinal Artery / pathology*
Retinal Diseases / diagnosis*
Retinal Diseases / pathology
Retinal Vein / pathology