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Fast Asymmetric Fronts Propagation for Image Segmentation

Chen, Da; Cohen, Laurent D. (2018), Fast Asymmetric Fronts Propagation for Image Segmentation, Journal of Mathematical Imaging and Vision, 60, 6, p. 766-783. 10.1007/s10851-017-0776-7

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1809.07987.pdf (5.476Mb)
Type
Article accepté pour publication ou publié
External document link
https://hal.archives-ouvertes.fr/hal-01963468
Date
2018
Journal name
Journal of Mathematical Imaging and Vision
Volume
60
Number
6
Publisher
Springer
Pages
766-783
Publication identifier
10.1007/s10851-017-0776-7
Metadata
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Author(s)
Chen, Da
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Cohen, Laurent D.
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
In this paper, we introduce a generalized asymmetric fronts propagation model based on the geodesic distance maps and the Eikonal partial differential equations. One of the key ingredients for the computation of the geodesic distance map is the geodesic metric, which can govern the action of the geodesic distance level set propagation. We consider a Finsler metric with the Randers form, through which the asymmetry and anisotropy enhancements can be taken into account to prevent the fronts leaking problem during the fronts propagation. These enhancements can be derived from the image edge-dependent vector field such as the gradient vector flow. The numerical implementations are carried out by the Finsler variant of the fast marching method, leading to very efficient interactive segmentation schemes. We apply the proposed Finsler fronts propagation model to image segmentation applications. Specifically, the foreground and background segmentation is implemented by the Voronoi index map. In addition, for the application of tubularity segmentation, we exploit the level set lines of the geodesic distance map associated with the proposed Finsler metric providing that a thresholding value is given.
Subjects / Keywords
Finsler Metric; Randers Metric; Eikonal partial differential equation; Fast marching method; Image segmentation; Tubular structure segmentation

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