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An adaptive multi-level-sets active contour model based on block search

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Abstract

In order to better handle images with intensity inhomogeneity and noise, an adaptive multi-level set active contour model based on block search is proposed in this paper. This model first defines a multiple edge extension criterion for the input image to block the image and avoid the loss of image edge information; Then, the proposed adaptive block search method is used to find the level set that is considered redundant, and the remaining parts are fused to obtain a rough binary mask; Finally, the target is extracted by using the newly defined energy functional and the edge contours of the extracted binary mask. The experimental results show that the average jaccard similarity coefficients of the proposed model for segmenting images with intensity inhomogeneity and real images are 97.81% and 98.41%, respectively, and the accuracy of the segmentation results is higher than that of other models participating in the comparison. Similarly, the results of the ablation experiment once again validated the robustness of the proposed model.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

The work is supported by the National Key Research and Development Program of China (2022YFF0607001), Guangdong Basic and Applied Basic Research Foundation (2023A1515010993), Guangdong Provincial Key Laboratory of Human Digital Twin (2022B1212010004), Guangzhou City Science and Technology Research Projects (2023B01J0011), Jiangmen Science and Technology Research Projects (2021080200070009151), Shaoguan Science and Technology Research Project (230316116276286), Foshan Science and Technology Research Project(2220001018608).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, GuangZhou, China

    Zhiheng Zhou, Ming Deng & Mingyue Zhang

  2. Key Laboratory of Big Data and Intelligent Robot, South China University of Technology, GuangZhou, China

    Zhiheng Zhou, Ming Deng & Mingyue Zhang

  3. School of Computer and Information Engineering, Henan Normal University, XinXiang, China

    Guoqi Liu

  4. Department of Intelligent Manufacturing, Wuyi University, JiangMen, China

    Tianlei Wang

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  1. Zhiheng Zhou
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  2. Ming Deng
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  4. Tianlei Wang
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  5. Mingyue Zhang
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Correspondence to Zhiheng Zhou.

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Appendix A

Appendix A

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Zhou, Z., Deng, M., Liu, G. et al. An adaptive multi-level-sets active contour model based on block search. Multimed Tools Appl 83, 72371–72390 (2024). https://doi.org/10.1007/s11042-024-18465-9

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