Content-based retinal image retrieval using dual-tree complex wavelet transform

Content-based image retrieval methods aid physicians in diagnosing the early detection of diabetic retinopathy for preventing blindness. In this work, the combination of two dimensional dual-tree complex wavelet transform (DT-CWT) and generalized Gaussian density (GGD) model is used for feature extraction. Kull-back Leibler Divergence (KLD) computes the similarity measure between two feature set. Experimental dataset includes 1200 images from MESSIDOR database. The mean precision rates at five retrieved images are obtained as 78.23% and 53.70% for Macular Edema and Retinopathy classes, respectively. Results are promising and give an indication that the dual-tree complex wavelet transform is efficient for content-based retinal image retrieval problem.