A Diabetic Retinopathy Detection Using Fuzzy Local Information C-Mean and Kernel Metric with non-Euclidean distant metric

Abstract: Image processing plays a main role in the detection of the any kind of disease into any part of the body in medical field. Nowadays, medical imaging for extracting the diseases directly into a computer is getting more wide. Likewise, in the proposed system the focus is on to the diabetic retinopathy disease which is caused into retina of eye and further leads into the poor vision/blindness. Here, the exudate eye disease is segmented from an image by using the segmentation method. For segmentation the most widely used fuzzy clustering method is proposed. Fuzzy local information c-mean algorithm is used with introducing a kernel metric. It is further modified by using non-Euclidean distance metric and the parameters are calculated for justifying the best detection result.

Keywords: Diabetic Retinopathy Retinal Image, Exudate Eye Disease, KWFLICM algorithm, non- Euclidean distance metric