Clustering Detection Method of Hard Exudates in Fundus Image Based on Neighborhood Constraint Model

Hard exudates are important manifestations and diagnostic bases for diabetic retinopathy.To solve the problem of easy disturbance of hard exudates detection by image background and noise,a hard exudates clustering detection method based on neighborhood constraint model is proposed.Firstly,the detection area is set.The target detection function,which is defined by the gray and spatial information of pixels,is used to complete the clustering segmentation of the image by iterative calculation.Then the gray differences of the neighborhood are calculated,and the greatest gray change is used as the constraint condition of the similarity decision to determine whether each cluster image is hard exudates.The performance of the method is verified on the open eye image databases.The results show that the method can effectively identify and detect the possible hard exudation in the fundus image.The accuracy of the normal image reaches 90%,and the sensitivity and positive predictive value for hard exudates achieve 79%and 81%,respectively.The method is thus proved conducive to the computer-aided diagnosis of the fundus diseases.