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A weighted region-based level set method for image segmentation with intensity inhomogeneity

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  • Haiping Yu
  • Ping Sun
  • Fazhi He
  • Zhihua Hu

Abstract

Image segmentation is a fundamental task in image processing and is still a challenging problem when processing images with high noise, low resolution and intensity inhomogeneity. In this paper, a weighted region-based level set method, which is based on the techniques of local statistical theory, level set theory and curve evolution, is proposed. Specifically, a new weighted pressure force function (WPF) is first presented to flexibly drive the closed contour to shrink or expand outside and inside of the object. Second, a faster and smoother regularization term is added to ensure the stability of the curve evolution and that there is no need for initialization in curve evolution. Third, the WPF is integrated into the region-based level set framework to accelerate the speed of the curve evolution and improve the accuracy of image segmentation. Experimental results on medical and natural images demonstrate that the proposed segmentation model is more efficient and robust to noise than other state-of-the-art models.

Suggested Citation

  • Haiping Yu & Ping Sun & Fazhi He & Zhihua Hu, 2021. "A weighted region-based level set method for image segmentation with intensity inhomogeneity," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-18, August.
    • Handle: RePEc:plo:pone00:0255948
    DOI: 10.1371/journal.pone.0255948
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    References listed on IDEAS

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    1. Jingfei Hu & Hua Wang & Jie Wang & Yunqi Wang & Fang He & Jicong Zhang, 2021. "SA-Net: A scale-attention network for medical image segmentation," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-14, April.
    2. Liansheng Wang & Shusheng Li & Rongzhen Chen & Sze-Yu Liu & Jyh-Cheng Chen, 2016. "An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-19, June.
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