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

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 nonperfusion, 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.

A Review on Retinal Feature Segmentation Methodologies for Diabetic Retinopathy

“…Segmentation is a process of grouping together pixels that have similar attributes [7]. Segmentation can be classified as follows: Region Based, Edge Based, Threshold, Feature Based Clustering, and Model Based [8].…”

Diabetes Mechanisms, Detection and Complications Monitoring

“…As the diabetes retinopathy progresses, the number of blood vessels varies, and the exudates appear in the advanced DR stages [Yun et al, 2008;Acharya et al, 2011a]. Different image processing techniques have been used to extract blood vessels and exudates in DR subjects, and these techniques are explained in this section.…”

“…5 [Nayak et al, 2008;Acharya et al, 2011a;Acharya et al 2009;Acharya et al, 2011b]. The green www.intechopen.com component of the RGB (Red, Green Blue) blood vessel image is considered for this study.…”

“…Fig. 6 shows the block diagram of the exudates extraction in digital fundus images Nayak et al, 2008;Acharya et al, 2011a;Acharya et al, 2011b]. The green component of the original image is extracted and subjected to the morphological closing operation by using octagonal shaped structuring element.…”

Classification of visualization exudates fundus images results using support vector machine

“…The statistics indicate that the number will double in the future [1]. The disease is divided into two categories; namely Type I (insulin-dependent) and Type II (insulin-independent) [2][3].…”