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An active contour model based on adaptively variable exponent combining Legendre polynomial for image segmentation

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Abstract

Images with intensity inhomogeneity and blurred boundaries are common in image segmentation tasks, which inevitably result in many difficulties in accurate image segmentation. Massive active contour models (ACMs) have been proposed to solve the problems of intensity inhomogeneity or blurred boundaries respectively. However, there is almost no way to effectively solve the above two problems at the same time, and they are sensitive to the initial contour and noise, or their segmentation speed is relatively slow. In this paper, we propose an active contour model (ACM) based on adaptively variable exponent combining Legendre polynomial (LP) for image segmentation. First, the Legendre polynomial intensity (LPI) is defined, which employs a linear combination of Legendre basis functions for region intensity approximation. Second, an adaptively LPI term is defined, which adopts an adaptively variable exponent function as an acceleration term to drive the curve to quickly evolve to the object boundaries. Third, the distance regularization term is introduced into the active contour as a regularization term to eliminate the need for reinitialization and restrict the behavior of level set function (LSF). Experimental results show that our method offers robustness to gray unevenness, noise and initial curve placement, and adaptability to low contrast and blurred boundaries and outperforms other state-of-the-art algorithms.

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Notes

  1. All the computations are implemented with MATLAB R2018a.

  2. Available: https://isic-archive.com/.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61876026, Grant 62176027 and Grant 62102179; in part by the General Program of National Natural Science Foundation of Chongqing under Grant cstc2020jcyj msxmX0790; in part by the Fundamental Research Funds for the Central Universities under Grant 2021CDJJMRH-014; in part by the Guangxi Key Laboratory of Cryptography and Information Security under Grant GCIS201905; in part by the Human Resources and Social Security Bureau Project of Chongqing under Grant cx2020073; in part by the Huawei Project under Grant H20210586; in part by the Suzhou Institute of USTC under Grant H20201528; in part by the Ningbo Natural Science Foundation under Grant 202003N4307.

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400030, China

    Jiajie Zhu, Bin Fang, Mingliang Zhou, Futing Luo & Weizhi Xian

  2. State Key Lab of Internet of Things for Smart City, University of Macau, Macau, 999078, China

    Jiajie Zhu, Mingliang Zhou, Futing Luo & Weizhi Xian

  3. School of Computing and Data Engineering, NingboTech University, Ningbo, China

    Gang Wang

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  1. Jiajie Zhu
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  2. Bin Fang
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Correspondence to Bin Fang or Mingliang Zhou.

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Zhu, J., Fang, B., Zhou, M. et al. An active contour model based on adaptively variable exponent combining Legendre polynomial for image segmentation. Multimed Tools Appl 81, 27495–27522 (2022). https://doi.org/10.1007/s11042-022-12340-1

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