Automatic Detection of Exudates in Diabetic Retinopathy Images

Problem statement: Diabetic Retinopathy (DR) is globally the primary cause of visual impairment and blindness in diabetic patients. Retinal image is essential and crucial for ophthalmologists to diagnose diseases. Many of technique can achieve good performance on retinal feature are clearly visible. Unfortunately, it is a normal situation that the retinal images in Thailand are low-quality images. The existing algorithm cannot detect low-quality image. Therefore, this study is part of a larger effort to develop a new method for detection of exudates in low quality retinal image. Approach: In this study, we presented a new method towards the development for detecting exudates pathologies of DR. The color retinal images are segmented using Fuzzy C-Means (FCM) clustering and morphological methods and following key preprocessing step, i.e., color normalization, contrast enhancement, remove noise and color space selection. This enables its difference in our methods compared to other approach and the algorithm can achieve good performance even on low-quality retinal images. Result/Conclusion: The result shows that accuracy values increase when the FCM clustering is combined with morphological methods techniques. If any applications need to detect maximum number of exudates pixels or require execution speed, the FCM clustering technique could be used in isolation. However, if the applications require higher accuracy, the FCM clustering combined with morphological methods should be chosen. This system intends to help ophthalmologists in DR screening process to detect symptoms faster and more easily. This is not a final result application but it can be a preliminary diagnosis tool or decision support system for ophthalmologists. Human ophthalmologists are still needed for the cases where detection results are not very obvious.