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A. Sopharak, B. Uyyanonvara, S. Barman, T. H. Williamson, “Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods,” Computerized Medical Imaging and Graphics, vol. 32, no. 8, pp. 720–727, 2008.

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Article

Detection of Hard Exudates in Retinal Fundus Images based on Important Features Obtained from Local Image Descriptors

1Department of Computer Engineering, Faculty of Engineering, Karabuk University, Turkey

2Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Yıldırım Beyazıt University, Turkey

3Department of Ophthalmology, Faculty of Medicine, Hacettepe University, Turkey


Journal of Computer Sciences and Applications. 2016, Vol. 4 No. 3, 59-66
DOI: 10.12691/jcsa-4-3-2
Copyright © 2016 Science and Education Publishing

Cite this paper:
Kemal AKYOL, Şafak BAYIR, Baha ŞEN, Hasan B. ÇAKMAK. Detection of Hard Exudates in Retinal Fundus Images based on Important Features Obtained from Local Image Descriptors. Journal of Computer Sciences and Applications. 2016; 4(3):59-66. doi: 10.12691/jcsa-4-3-2.

Correspondence to: Kemal  AKYOL, Department of Computer Engineering, Faculty of Engineering, Karabuk University, Turkey. Email: kemalakyol48@gmail.com

Abstract

Diabetic retinopathy is one of the main complications of diabetes mellitus and it is a progressive ocular disease, the most significant factor contributing to blindness in the later stages of the disease. It has been a subject of many studies in the medical image processing field for a long time. Hard exudates are one of the primary signs of early stage diabetic retinopathy diagnosis. Immediately identifying hard exudates is of great importance for the blindness and coexistent retinal edema. There are various ways of achieving meaningful information from an image and one of them is key point extraction method. In this study, we presented a technique based on the acquisition of important information by utilizing the description information about the image within the framework of the learning approach in order to identify hard exudates. This technique includes the learning and testing processes of the system in order to make the right decisions in the analysis of new retinal fundus images. We performed experimental validation on DIARETDB1 dataset. The obtained results showed us the positive effects of machine learning technique suggested by us for the detection of hard exudates.

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