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, KZ Widhia Oktoeberza, Teguh Bharata Adji, Faisal Najamuddin

 

Abstract: 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.
Keywords: Fundus images, Exudates, Texture feature, GLCM, Lacunarity,

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