Abstract:
Content-based image retrieval methods aid physicians in diagnosing the early detection of diabetic retinopathy for preventing blindness. In this work, the combination of ...Show MoreMetadata
Abstract:
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.
Published in: 2013 International Conference on Signal Processing , Image Processing & Pattern Recognition
Date of Conference: 07-08 February 2013
Date Added to IEEE Xplore: 15 April 2013
ISBN Information: