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In this paper, we propose a novel approach to improve microaneurysm detection in color fundus images by clustering image databases since they usually contain images with different characteristics. Thus, a parameter setting of an algorithm determined for a database is not necessarily optimal on another one. To overcome this problem, we determine clusters of retinal images coming from different sources. In other words, we consider individual image characteristics instead of databases in a detection problem. We select 19 similarity measures to calculate image differences, and apply k-means clustering to obtain the clusters. For each cluster, an optimal parameter setting is determined for the same microaneurysm detector. We tested our approach on a publicly available database, where the performance of a state-of-the-art microaneurysm detector is successfully increased by the proposed method.