Localising Microaneurysms in Fundus Images Through Singular Spectrum Analysis

Goal: Reliable recognition of microaneurysms is an essential task when developing an automated analysis system for diabetic retinopathy detection. In this work, we propose an integrated approach for automated microaneurysm detection with high accuracy. Methods: Candidate objects are first located by applying a dark object filtering process. Their cross-section profiles along multiple directions are processed through singular spectrum analysis. The correlation coefficient between each processed profile and a typical microaneurysm profile is measured and used as a scale factor to adjust the shape of the candidate profile. This is to increase the difference in their profiles between true microaneurysms and other non-microaneurysm candidates. A set of statistical features of those profiles is then extracted for a K-Nearest Neighbour classifier. Results: Experiments show that by applying this process, microaneurysms can be separated well from the retinal background, the most common interfering objects and artefacts. Conclusion: The results have demonstrated the robustness of the approach when testing on large scale datasets with clinically acceptable sensitivity and specificity. Significance: The approach proposed in the evaluated system has great potential when used in an automated diabetic retinopathy screening tool or for large scale eye epidemiology studies