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A fast algorithm for image segmentation based on fuzzy region competition

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Abstract

Variational models for image segmentation are usually solved by the level set method, which is not only slow to compute but also dependent on initialization strongly. Recently, fuzzy region competition models or globally convex segmentation models have been introduced. They are insensitive to initialization, but contain TV-regularizers, making them difficult to compute. Goldstein, Bresson and Osher have applied the split Bregman iteration to globally convex segmentation models which avoided the regularization of TV norm and speeded up the computation. However, the split Bregman method needs to solve a partial differential equation (PDE) in each iteration. In this paper, we present a simple algorithm without solving the PDEs proposed originally by Jia et al. (2009) with application to image segmentation problems. The algorithm also avoids the regularization of TV norm and has a simpler form, which is in favor of implementing. Numerical experiments show that our algorithm works faster and more efficiently than other fast schemes, such as duality based methods and the split Bregman scheme.

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Authors and Affiliations

  1. School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, 310018, China

    Fangfang Dong

  2. Department of Mathematics, Hangzhou Normal University, Hangzhou, 310036, China

    Chunxiao Liu

  3. Center of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China

    De-Xing Kong

Authors
  1. Fangfang Dong
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  2. Chunxiao Liu
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  3. De-Xing Kong
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Corresponding author

Correspondence to Fangfang Dong.

Additional information

Communicated by R. Q. Jia.

This work was supported in part by the NNSF of China (Grant No. 10971190) and the Qiu-Shi Chair Professor Fellowship from Zhejiang University.

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Dong, F., Liu, C. & Kong, DX. A fast algorithm for image segmentation based on fuzzy region competition. Adv Comput Math 37, 521–542 (2012). https://doi.org/10.1007/s10444-011-9221-4

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  • DOI: https://doi.org/10.1007/s10444-011-9221-4

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