Automated Detection and Counting of Hard Exudates for Diabetic Retinopathy by using Watershed and Double Top-Bottom Hat Filtering Algorithm

Dafwen Toresa - Faculty of computer science, Lancang Kuning University, Pekanbaru, Riau, Indonesia
Mohamad Shahril - Data Science Research Lab, School of Computing, Universiti Utara Malaysia, Malaysia
Nor Harun - Data Science Research Lab, School of Computing, Universiti Utara Malaysia, Malaysia
Juhaida Bakar - Data Science Research Lab, School of Computing, Universiti Utara Malaysia, Malaysia
Hidra Amnur - Depatment of Information Technology, Politeknik Negeri Padang, Padang, West Sumatera, Indonesia

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Diabetic Retinopathy (DR) is one of diabetes complications that affects our eyes. Hard Exudate (HE) are known to be the early signs of DR that potentially lead to blindness. Detection of DR automatically is a complicated job since the size of HE is very small. Besides, our community nowadays lack awareness on diabetic where they do not know that diabetes can affect eyes and lead to blindness if regular check-up is not performed. Hence, automated detection of HE known as Eye Retinal Imaging System (EyRis) was created to focus on detecting the HE based on fundus image. The purpose of this system development is for early detection of the symptoms based on retina images captured using fundus camera. Through the captured retina image, we can clearly detect the symptoms that lead to DR. In this study, proposed Watershed segmentation method for detecting HE in fundus images. Top-Hat and Bottom-Hat were use as enhancement technique to improve the quality of the image. This method was tested on 15 retinal images from the Universiti Sains Malaysia Hospital (HUSM) at three different stages: Normal, NPDR, and PDR. Ten of these images have abnormalities, while the rest are normal retinal images. The evaluation of the segmentation images would be compared by Sensitivity, F-score and accuracy based on medical expert's hand drawn ground truth. The results achieve accuracy 0.96 percent with 0.99 percent sensitivity for retinal images.


Diabetes; diabetic retinopathy; image segmentation; hard exudates; fundus images.

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