Globally Optimal Finsler Active Contours

Authors:
Christopher Zach;Liang Shan;Marc Niethammer
Affiliations:
University of North Carolina at Chapel Hill,;University of North Carolina at Chapel Hill,;University of North Carolina at Chapel Hill,
Venue:
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Year:
2009

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Abstract

We present a continuous and convex formulation for Finsler active contours using seed regions or utilizing a regional bias term. The utilization of general Finsler metrics instead of Riemannian metrics allows the segmentation boundary to favor appropriate locations (e.g. with strong image discontinuities) and suitable directions (e.g. aligned with dark to bright image gradients). Strong edges are not required everywhere along the desired segmentation boundary due to incorporation of a regional bias. The resulting optimization procedure is simple and efficient, and leads to binary segmentation results regardless of the underlying continuous formulation. We demonstrate the proposed method in several examples.