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. 2015 Dec;28(6):761-8.
doi: 10.1007/s10278-015-9793-5.

Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System

T Jaya  1 , J Dheeba  2 , N Albert Singh  3
Affiliations

Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System

T Jaya et al. J Digit Imaging. 2015 Dec.

Abstract

Diabetic retinopathy is a major cause of vision loss in diabetic patients. Currently, there is a need for making decisions using intelligent computer algorithms when screening a large volume of data. This paper presents an expert decision-making system designed using a fuzzy support vector machine (FSVM) classifier to detect hard exudates in fundus images. The optic discs in the colour fundus images are segmented to avoid false alarms using morphological operations and based on circular Hough transform. To discriminate between the exudates and the non-exudates pixels, colour and texture features are extracted from the images. These features are given as input to the FSVM classifier. The classifier analysed 200 retinal images collected from diabetic retinopathy screening programmes. The tests made on the retinal images show that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and features sets, the area under the receiver operating characteristic curve reached 0.9606, which corresponds to a sensitivity of 94.1% with a specificity of 90.0%. The results suggest that detecting hard exudates using FSVM contribute to computer-assisted detection of diabetic retinopathy and as a decision support system for ophthalmologists.

Keywords: Colour fundus images; Diabetic retinopathy; Fuzzy support vector machine; Hard exudates; Laws texture energy measures.

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Figures

Fig. 1
Fig. 1
The proposed diabetic retinopathy detection system model
Fig. 2
Fig. 2
Example colour fundus image. a Normal retinal image. b Retinal image with exudates
Fig. 3
Fig. 3
Automated feature analysis of the colour fundus image. a Colour fundus image. b R component. c G component
Fig. 4
Fig. 4
Detection of hard exudates using FSVM classifier. a Original colour fundus image. b Grey scale image. c Optic disc segmentation. d Hard exudates detection
Fig. 5
Fig. 5
ROC curve for SVM and FSVM classifier
See this image and copyright information in PMC

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