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Regularized Level Set Method by Incorporating Local Statistical Information and Global Similarity Compatibility for Image Segmentation

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

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Abstract

This paper presents a regularized level set method for image segmentation, where the local statistical information and global similarity compatibility are both incorporated into the construction of energy functional. By considering the image local statistical information, the proposed model can efficiently segment images with intensity inhomogeneity. To improve the convergence speed, an adaptive stop strategy is proposed. In addition, the distance regularization term is defined with a five power of polynomial function for maintaining the stability during the curve evolution. Finally, experimental results show that our proposed model is efficient for segmenting noisy images, texture images and images with intensity inhomogeneity.

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Authors and Affiliations

  1. Faculty of Information Engineering, City College Wuhan University of Science and Technology, Wuhan, 430083, China

    Yu Haiping & Zhang Huali

Authors
  1. Yu Haiping
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  2. Zhang Huali
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Corresponding authors

Correspondence to Yu Haiping or Zhang Huali .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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Haiping, Y., Huali, Z. (2015). Regularized Level Set Method by Incorporating Local Statistical Information and Global Similarity Compatibility for Image Segmentation. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_38

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  • DOI: https://doi.org/10.1007/978-3-319-22180-9_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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