• Vol 6, No 2 (2015)
  • Electrical, Electronics, and Computer Engineering

Detection of Exudates on Color Fundus Images using Texture Based Feature Extraction

Hanung Adi Nugroho, K.Z. Widhia Oktoeberza, Teguh Bharata Adji, Faisal Najamuddin


Cite this article as:

Nugroho, H.A., Oktoeberza, K.W., Adji, T.B., Najamuddin, F., 2015. Detection of Exudates on Color Fundus Images using Texture Based Feature Extraction. International Journal of Technology. Volume 6(2), pp. 121-129

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Hanung Adi Nugroho Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika No.2 Campus UGM, Yogyakarta 55281, Indonesia
K.Z. Widhia Oktoeberza Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika No.2 Campus UGM, Yogyakarta 55281, Indonesia
Teguh Bharata Adji Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika No.2 Campus UGM, Yogyakarta 55281, Indonesia
Faisal Najamuddin Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika No.2 Campus UGM, Yogyakarta 55281, Indonesia
Email to Corresponding Author

Abstract
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World Health Organisation (WHO) has predicted 300 million peoples will suffer of diabetic in 2025. Long-term diabetics can lead to diabetic retinopathy that can cause blindness in developing countries. One of the abnormalities of diabetic retinopathy is exudate. Exudates are classified into two categories, i.e. hard and soft exudates. This paper proposes feature extraction based on texture for distinguishing hard, soft and non-exudates. The green channel of the original images is enhanced by CLAHE and followed by median filtering and thresholding in red channel to detect and remove the optic disc. The enhanced image is segmented based on clustering to obtain the region of interest of exudates. Feature extraction based on texture is conducted by using GLCM and lacunarity. Results show that classification based on NaïveBayes algorithm achieves accuracy, specificity and sensitivity of 92.13%, 96% and 87.18%, respectively.

Fundus images, Exudates, Texture feature, GLCM, Lacunarity,

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