Optimal Filter Framework for Automated, Instantaneous Detection of Lesions in Retinal Images

Automated detection of lesions in retinal images is a crucial step towards efficient early detection, or screening, of large at-risk populations. In particular, the detection of microaneurysms, usually the first sign of diabetic retinopathy (DR), and the detection of drusen, the hallmark of age-related macular degeneration (AMD), are of primary importance. In spite of substantial progress made, detection algorithms still produce 1) false positives-target lesions are mixed up with other normal or abnormal structures in the eye, and 2) false negatives-the large variability in the appearance of the lesions causes a subset of these target lesions to be missed. We propose a general framework for detecting and characterizing target lesions almost instantaneously. This framework relies on a feature space automatically derived from a set of reference image samples representing target lesions, including atypical target lesions, and those eye structures that are similar looking but are not target lesions. The reference image samples are obtained either from an expert-or a data-driven approach. Factor analysis is used to derive the filters generating this feature space from reference samples. Previously unseen image samples are then classified in this feature space. We tested this approach by training it to detect microaneurysms. On a set of images from 2739 patients including 67 with referable DR, DR detection area under the receiver-operating characteristic curve (AUC) was comparable (AUC = 0.927) to our previously published red lesion detection algorithm (AUC = 0.929). We also tested the approach on the detection of AMD, by training it to differentiate drusen from Stargardt's disease lesions, and achieved an AUC = 0.850 on a set of 300 manually detected drusen and 300 manually detected flecks. The entire image processing sequence takes less than a second on a standard PC compared to minutes in our previous approach, allowing instantaneous detection. Free-response receiver-operating characteristic analysis showed the superiority of this approach over a framework where false positives and the atypical lesions are not explicitly modeled. A greater performance was achieved by the expert-driven approach for DR detection, where the designer had sound expert knowledge. However, for both problems, a comparable performance was obtained for both expert- and data-driven approaches. This indicates that annotation of a limited number of lesions suffices for building a detection system for any type of lesion in retinal images, if no expert-knowledge is available. We are studying whether the optimal filter framework also generalizes to the detection of any structure in other domains.