Boosting hand-crafted features for curvilinear structure segmentation by learning context filters

Abstract

Combining hand-crafted features and learned filters (i.e. feature boosting) for curvilinear structure segmentation has been proposed recently to capture key structure configurations while limiting the number of learned filters. Here, we present a novel combination method pairing hand-crafted appearance features with learned context filters. Unlike recent solutions based only on appearance filters, our method introduces context information in the filter learning process. Moreover, it reduces the potential redundancy of learned appearance filters that may be reconstructed using a combination of hand-crafted filters. Finally, the use of k-means for filter learning makes it fast and easily adaptable to other datasets, even when large dictionary sizes (e.g. 200 filters) are needed to improve performance. Comprehensive experimental results using 3 challenging datasets show that our combination method outperforms recent state-of-the-art HCFs and a recent combination approach for both performance and computational time.