Automatic exudates detection in fundus image using intensity thresholding and morphology

Diabetic retinopathy (DR) is a leading cause of blindness in diabetic patients. Exudates are one of the most common earliest signs of diabetic retinopathy. Automatic and accurate detection of exudates in fundus images is an important step in early diagnosis of DR. In the proposed method detection of exudates, two independent approaches based on intensity thresholding and morphological processing are strategically combined to detect any small exudates present while removing all possible types of false positives. This strategic combination removes the noise sources from blood vessels and reflections during image capture making the detection of exudates accurate. Experimental results indicate that the proposed method has good accuracy in exudates detection without compromising the computational time and hence can be considered for screening purpose of DR.