CDR in Glaucoma Detection using Dissimilarity Constraints Coding

Abstract

Glaucoma is an irreversible chronic eye disease that leads to vision loss. As it can be slowed down through treatment, detecting the disease in time is important. However, many patients are unaware of the disease because it progresses slowly without easily noticeable symptoms. Currently, there is no effective method for low cost population-based glaucoma detection or screening. Recent studies have shown that automated optic nerve head assessment from 2D retinal fundus images is promising for low cost glaucoma screening. In this paper, we propose a method for cup to disc ratio (CDR) assessment using 2D retinal fundus images. Methods: In the proposed method, the optic disc is first segmented and reconstructed using a novel sparse dissimilarity-constrained coding (SDC) approach which considers both the dissimilarity constraint and the sparsity constraint from a set of reference discs with known CDRs. Subsequently, the reconstruction coefficients from the SDC are used to compute the CDR for the testing disc. Results: The proposed method has been tested for CDR assessment in a database of 650 images with CDRs manually measured by trained professionals previously. Experimental results show an average CDR error of 0.064 and correlation coefficient of 0.67 compared with the manual CDRs, better than the state-of-the-art methods. Our proposed method has also been tested for glaucoma screening. The method achieves areas under curve of 0.83 and 0.88 on datasets of 650 and 1676 images, respectively, outperforming other methods. Conclusion: The proposed method achieves good accuracy for glaucoma detection. Significance: The method has a great potential to be used for large-scale population-based glaucoma screening.