Localizing Microaneurysms in Fundus Images Through Singular Spectrum Analysis.

GOAL: Reliable recognition of microaneurysms (MAs) is an essential task when developing an automated analysis system for diabetic retinopathy (DR) detection. In this study, we propose an integrated approach for automated MA 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 MA 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 MAs and other non-MA candidates. A set of statistical features of those profiles is then extracted for a K-nearest neighbor classifier.

RESULTS: Experiments show that by applying this process, MAs can be separated well from the retinal background, the most common interfering objects and artifacts.

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 DR screening tool or for large scale eye epidemiology studies.