Abstract
Diabetic Retinopathy (DR) is an eye disease caused by diabetic complications that have early signs of a disease containing microaneurysms and exudates. Diabetic retinopathy for a long time can cause vision loss (blindness). So automatic detection is needed. Therefore we conduct research for the detection of exudates based on segmentation using the STARE and DIARETDB1 datasets. The exudate appears yellowish and glowing in the background of the retina with irregular size and shape. The use of several segmentation methods can be done in exudate detection. The method used is the adaptive threshold method, multi-threshold otsu, top-hat and bottom hat, and fuzzy c-means performance. The average performance results of several methods used in segmenting for each image in the STARE dataset are otsu multi threshold 87.1%, adaptive threshold of 89.9%, top-hat and bottom hat 87.7% and fuzzy c-means 95.4%. and in the DIARETDB1 dataset, otsu multi threshold is 89%, adaptive threshold is 88.2%, top-hat and bottom hat 92.9% and fuzzy c-means 90.6%. These results indicate that the proposed method can provide good exudate segmentation results.
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