Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Design and study of flux-based features for 3D vascular tracking
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
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We present a unified coarse-to-fine approach for extracting the medial axis representations (centerlines) of human vasculature in contrast enhanced (CE)-CTA/MRA. The proposed method constitutes two separate analysis stages that are successively applied (and repeated) for a refined extraction. The former stage involves the use of a graphbased optimization algorithm that identifies the minimum-cost paths between user-specified seed points. The costs of all feasible paths are efficiently computed via the medialness filter, which is a contrast- and scale-invariant local operator sensitive to the presence of tubular structures. Nonetheless, image noise and the presence of nearby blood vessels can affect the quality of detection and delineation. In the latter stage, we thereby employ a novel multiscale orientation descriptor so as to guide/stop additional minimal path extraction steps. Specifically, the descriptor is designed to classify a point of interest as vessel or non-vessel, as well as to obtain a reliable estimate of the number and directions of the vascular segments (branches) at a vessel point. Our method improves the accuracy of extraction by robustly identifying critical configurations such as bifurcations, endpoints, or non-vessel points, and thereby delineating/ eliminating missing/spurious vessel branches.