Abstract
While geometric deformable models have brought tremendous impacts on shape representation and analysis in medical image analysis, some of the remaining problems include the handling of boundary leakage and the lack of global understanding of boundaries. We present a modification to the geodesic active contour framework such that in.uence from local neighbors of a front point is explicitly incorporated, and it is thus capable of robustly dealing with the boundary leakage problem. The fundamental power of this strategy rests with the local integration of evolution forces for each front point within its local in.uence domain, which is adaptively determined by the local level set geometry and image/ prior information. Due to the combined e.ects of internal and external constraints on a point and the interactions with those of its neighbors, our method allows stable boundary detection when the edge information is noisy and possibly discontinuous (e.g. gaps in the boundaries) while maintaining the abilities to handle topological changes, thanks to the level set implementation. The algorithm has been implemented using the meshfree particle domain representation, and experimental results on synthetic and real images demonstrate its superior performance.
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© 2005 Springer-Verlag Berlin Heidelberg
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Liu, H., Chen, Y., Ho, H.P., Shi, P. (2005). Geodesic Active Contours with Adaptive Neighboring Influence. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3750. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566489_91
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DOI: https://doi.org/10.1007/11566489_91
Publisher Name: Springer, Berlin, Heidelberg
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