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A More Robust Active Contour Model with Group Similarity

  • Shaozhu Chen
  • Xiaodong Zhao
  • Xianhui LiuEmail author
  • Yufei Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10361)

Abstract

Image segmentation based on active contour model has been widely used in recent years. However, in real application, image segmentation results are always leaded to corrupt because of partial missing of target boundaries or some misleading factors. In order to solve the issue, Zhou proposed a model called active contour with group similarity. But Zhou’s model is purely based on global information of image and cannot deal with image segmentation with intensity inhomogeneity. In order to optimize Zhou’s model and make it more robust, we construct a new energy function which combines global feature with local feature of image. The local feature can solve the issue of image segmentation with intensity inhomogeneity. And the global feature can make our model less sensitive to the initial position of the curve or noise. The experiment results have proved that, our method can achieve satisfying segmentation results on image segmentation with intensity inhomogeneity and show less sensitive to the initial position of the curve.

Keywords

CV model LBF model Group similarity Robust Global information Local information 

Notes

Acknowledgement

This work was supported by the National Key Technology Support Program of China (No. 2015BAF04B00), The Fundamental Research Funds for the central Universities, and the Shanghai Innovation Action Project of Science and Technology (15DZ1101202).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shaozhu Chen
    • 1
  • Xiaodong Zhao
    • 1
  • Xianhui Liu
    • 1
    Email author
  • Yufei Chen
    • 1
  1. 1.CAD Research CenterTongji UniversityShanghaiPeople’s Republic of China

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