Computer-based detection of diabetes retinopathy stages using digital fundus images

• Published in: Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine Journal of engineering in medicine, 2009-07, Vol.223 (5), p.545-553
• Main Authors: Acharya, U R, Lim, C M, Ng, E Y K, Chee, C, Tamura, T
• Format: Article
• Language: English
• Subjects: Adult; Aged; Algorithms; Aneurysms; Artificial Intelligence; Automation; Biomedical engineering; Blindness; Blood vessels; Cardiomyopathy; Classification; Complications; Diabetes; Diabetes mellitus; Diabetic neuropathy; Diabetic retinopathy; Diabetic Retinopathy - pathology; Diagnosis; Digital computers; Digital imaging; Exudates; Exudation; Eye; Feature extraction; Female; Humans; Image classification; Image detection; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image processing systems; Male; Medical technology; Microvasculature; Middle Aged; Morphology; Nephropathy; Pattern Recognition, Automated - methods; Perfusion; Permeability; Reproducibility of Results; Retina; Retinopathy; Retinoscopy - methods; Screening; Sensitivity; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Support vector machines; Temporal variations
• ISSN: 0954-4119; 2041-3033
• DOI: 10.1243/09544119JEIM486

Abstract Diabetes mellitus is a heterogeneous clinical syndrome characterized by hyperglycaemia and the long-term complications are retinopathy, neuropathy, nephropathy, and cardiomyopathy. It is a leading cause of blindness. Diabetic retinopathy is the progressive pathological alterations in the retinal microvasculature, leading to areas of retinal non-perfusion, increased vascular permeability, and the pathological proliferation of retinal vessels. Hence, it is beneficial to have regular cost-effective eye screening for diabetes subjects. Nowadays, different stages of diabetes retinopathy are detected by retinal examination using indirect biomicroscopy by senior ophthalmologists. In this work, morphological image processing and support vector machine (SVM) techniques were used for the automatic diagnosis of eye health. In this study, 331 fundus images were analysed. Five groups were identified: normal retina, mild non-proliferative diabetic retinopathy, moderate non-proliferative diabetic retinopathy, severe non-proliferative diabetic retinopathy, and proliferative diabetic retinopathy. Four salient features blood vessels, microaneurysms, exudates, and haemorrhages were extracted from the raw images using image-processing techniques and fed to the SVM for classification. A sensitivity of more than 82 per cent and specificity of 86 per cent was demonstrated for the system developed.