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Locally Enhanced Chan-Vese Model with Anisotropic Mesh Adaptation for Intensity Inhomogeneous Image Segmentation

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Intelligent Systems and Applications (IntelliSys 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 824))

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Abstract

Chan-Vese (CV) model is a well-known mathematical model for image segmentation; however, it has difficulty handling images with inhomogeneous intensity. Many models have been proposed to address this difficulty. In this paper, we propose a locally enhanced Chan-Vese model (LECV) to successfully segment images with intensity inhomogeneity. We define a new signed pressure force (SPF) function based on the local image information from a triangular mesh representation. The anisotropic mesh representation (AMA representation) of the image also helps improving the computational accuracy and efficiency. Numerical results demonstrate that our proposed LECV model provides better segmentation for images with inhomogeneous intensity than the traditional Chan-Vese model as well as a few commonly used models.

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Author information

Authors and Affiliations

  1. University of Kufa, Najaf, Iraq

    Karrar K. Abbas

  2. Arizona State University, Tempe, AZ, 85281, USA

    Xianping Li

Authors
  1. Karrar K. Abbas
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  2. Xianping Li
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Correspondence to Xianping Li .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Abbas, K.K., Li, X. (2024). Locally Enhanced Chan-Vese Model with Anisotropic Mesh Adaptation for Intensity Inhomogeneous Image Segmentation. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 824. Springer, Cham. https://doi.org/10.1007/978-3-031-47715-7_9

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