Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review

Authors:
Oliver Faust;Rajendra Acharya U.;E. Y. Ng;Kwan-Hoong Ng;Jasjit S. Suri
Affiliations:
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore 599489;Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore 599489;School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798;Department of Biomedical Imaging, University of Malaya, Kuala Lumpur, Malaysia;Biomedical Technologies, Denver, USA and University of Idaho, Moscow, USA
Venue:
Journal of Medical Systems
Year:
2012

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Abstract

Diabetes is a chronic end organ disease that occurs when the pancreas does not secrete enough insulin or the body is unable to process it properly. Over time, diabetes affects the circulatory system, including that of the retina. Diabetic retinopathy is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. Ophthalmologists recognize diabetic retinopathy based on features, such as blood vessel area, exudes, hemorrhages, microaneurysms and texture. In this paper we review algorithms used for the extraction of these features from digital fundus images. Furthermore, we discuss systems that use these features to classify individual fundus images. The classifications efficiency of different DR systems is discussed. Most of the reported systems are highly optimized with respect to the analyzed fundus images, therefore a generalization of individual results is difficult. However, this review shows that the classification results improved has improved recently, and it is getting closer to the classification capabilities of human ophthalmologists.