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Image segmentation by phase-field models with local information

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Abstract

We proposed an improved phase-field model with local information for image segmentation. In our approach, a new double well potential function containing local contributions is taken into account. The advantages of the new scheme are in two folders. Firstly, the affection of noise could be reduced in image segmentation with intensity inhomogeneity. Secondly, a more stable boundary evolution could be achieved compared with the traditional way. Segmentation results for several different types of images are reported. We compare the segmentation results with the classical model for two-dimensional images. In the segmentation of three-dimensional magnetic resonance brain images, the accuracy comparisons are presented by the Dice and Jaccard similarity coefficients. Compared with the classical model, our model has higher segmentation accuracy and better stability of boundary evolution.

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Authors and Affiliations

  1. School of Mathematical Science, Zhejiang University, 310027, Hangzhou, China

    Jinyue Chen, Silu Chen & Xianliang Hu

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  1. Jinyue Chen
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  2. Silu Chen
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  3. Xianliang Hu
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Correspondence to Xianliang Hu.

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Chen, J., Chen, S. & Hu, X. Image segmentation by phase-field models with local information. Multimed Tools Appl 81, 3039–3057 (2022). https://doi.org/10.1007/s11042-021-11718-x

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  • DOI: https://doi.org/10.1007/s11042-021-11718-x

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