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A Novel Active Contour Model Using Oriented Smoothness and Infinite Laplacian for Medical Image Segmentation

  • Chunhong CaoEmail author
  • Chengyao Zhou
  • Jie Yu
  • Kai Hu
  • Fen Xiao
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

Active contour model (ACM) has been widely used in image segmentation. The original ACM has poor weak edge preservation ability and it is difficult to converge to the concave, especially long and thin indentation convergence. In order to address these defects, a series of models such as gradient vector flow (GVF) and general gradient vector flow (GGVF) were proposed. A new edge-preserving ACM using oriented smoothness, infinite Laplacian is proposed in this paper to further address these issues. Oriented smoothness and infinite Laplacian are adopted as the smoothness term in the energy function to promote the model’s weak edge preservation and concave convergence ability. Furthermore, we employ a component-based normalization to accelerate the concave convergence rate. The experimental results show that the proposed method achieves better performance than the other comparative methods.

Keywords

Active contour model Medical image segmentation Oriented smoothness Infinite Laplacian 

Notes

Acknowledgements

This work was supported by the NSFC under Grants 61401386, 61802328.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Chunhong Cao
    • 1
    Email author
  • Chengyao Zhou
    • 1
  • Jie Yu
    • 1
  • Kai Hu
    • 1
  • Fen Xiao
    • 1
  1. 1.Key Laboratory of Intelligent Computing and Information Processing of Ministry of EducationXiangtan UniversityXiangtanChina

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