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Fast Global Minimization of the Active Contour/Snake Model

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Abstract

The active contour/snake model is one of the most successful variational models in image segmentation. It consists of evolving a contour in images toward the boundaries of objects. Its success is based on strong mathematical properties and efficient numerical schemes based on the level set method. The only drawback of this model is the existence of local minima in the active contour energy, which makes the initial guess critical to get satisfactory results. In this paper, we propose to solve this problem by determining a global minimum of the active contour model. Our approach is based on the unification of image segmentation and image denoising tasks into a global minimization framework. More precisely, we propose to unify three well-known image variational models, namely the snake model, the Rudin–Osher–Fatemi denoising model and the Mumford–Shah segmentation model. We will establish theorems with proofs to determine the existence of a global minimum of the active contour model. From a numerical point of view, we propose a new practical way to solve the active contour propagation problem toward object boundaries through a dual formulation of the minimization problem. The dual formulation, easy to implement, allows us a fast global minimization of the snake energy. It avoids the usual drawback in the level set approach that consists of initializing the active contour in a distance function and re-initializing it periodically during the evolution, which is time-consuming. We apply our segmentation algorithms on synthetic and real-world images, such as texture images and medical images, to emphasize the performances of our model compared with other segmentation models.

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Authors and Affiliations

  1. Signal Processing Institute, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland

    Xavier Bresson, Pierre Vandergheynst & Jean-Philippe Thiran

  2. Department of Mathematics, University of Michigan, Ann Arbor, MI, USA

    Selim Esedoḡlu

  3. Department of Mathematics, University of California, Los Angeles, CA, USA

    Stanley Osher

Authors
  1. Xavier Bresson
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  2. Selim Esedoḡlu
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  3. Pierre Vandergheynst
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  4. Jean-Philippe Thiran
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  5. Stanley Osher
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Corresponding author

Correspondence to Xavier Bresson.

Additional information

Research supported by NIH U54RR021813, NSF DMS-0312222, NSF ACI-0321917 and NSF DMI-0327077.

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Bresson, X., Esedoḡlu, S., Vandergheynst, P. et al. Fast Global Minimization of the Active Contour/Snake Model. J Math Imaging Vis 28, 151–167 (2007). https://doi.org/10.1007/s10851-007-0002-0

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