An Improved Segmentation Technique for Early Detection of Exudates of Diabetic Retinopathy Disease

Abstract

Diabetic retinopathy (DR) is a diabetes- caused disease that is associated with  leakage of fluid from the blood vessels into the retina, leading to its damage. It is one of the most common diseases that can lead to weak vision and even blindness. Exudates is a clear indication of diabetic retinopathy, which is the main cause of blindness in people with diabetes. Therefore, early detection of exudates is a crucial and essential step to prevent blindness and vision loss is in the analysis of digital diabetic retinopathy systems. This paper presents an improved approach for detection of exudates in retina image using supervised-unsupervised Minimum Distance (MD) segmentation method. The suggested system includes three stages; First, after image acquisition, it is pre-processed for preparing and improving its quality. Second, the simple toward interpretation and analysis of image is segmentation as another stage.
     In this research, we presented a method which is used for segmentation of     exudates by the adaptive (supervised-unsupervised) Minimum Distance (MD)  creation segmentation algorithm with two non-overlapping clusters. The method was proposed based on its performance compared with other methods and followed by exudates extraction as a final stage. This proposed framework helps the ophthalmologists to distinguish the problem earlier, which enables them to recommend a superior medication for forestalling further retinal harm.