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Improving image segmentation based on patch-weighted distance and fuzzy clustering

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Abstract

Image segmentation is the basis of image analysis, object tracking, and other fields. However, image segmentation is still a bottleneck due to the complexity of images. In recent years, fuzzy clustering is one of the most important selections for image segmentation, which can retain information as much as possible. However, fuzzy clustering algorithms are sensitive to image artifacts. In this study, an improved image segmentation algorithm based on patch-weighted distance and fuzzy clustering is proposed, which can be divided into two steps. First, the pixel correlation between adjacent pixels is retrieved based on patch-weighted distance, and then the pixel correlation is used to replace the influence of neighboring information in fuzzy algorithms, thereby enhancing the robustness. Experiments on simulated, natural and medical images illustrate that the proposed schema outperforms other fuzzy clustering algorithms.

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Acknowledgments

The authors would like to thank anonymous reviewers for their valuable comments and suggestions which lead to substantial improvements of this paper. Also, the authors would like to thank Dr. Krindis for providing the source codes and experimental pictures of FLICM. We would also thank Dr. Weiling Cai for providing the codes of FCMS1, FCMS2 and FGFCM. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation significantly.

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Authors and Affiliations

  1. School of Information and Electrical Engineering, Ludong University, Yantai, 264025, China

    Xiaofeng Zhang, Yujuan Sun & Hua Wang

  2. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China

    Muwei Jian

  3. Shandong Co-Innovation Center of Future Intelligent Computing, Shandong Technology and Business University, Yantai, 264005, China

    Xiaofeng Zhang & Caiming Zhang

  4. Shandong Province Key Lab of Digital Media Technology, Shandong University of Finance and Economics, Jinan, 250061, China

    Xiaofeng Zhang & Caiming Zhang

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  1. Xiaofeng Zhang
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  2. Muwei Jian
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  3. Yujuan Sun
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  4. Hua Wang
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  5. Caiming Zhang
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Correspondence to Xiaofeng Zhang.

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The research was supported by NSF of China under grant numbers 61873117, 61602229, 61873145, 61772253 and 61771231, NSFC Joint Fund with Zhejiang Integration of Informatization and Industrialization under Key Project grant number U1609218, the Natural Science Foundation of Shandong Province grant numbers ZR2016FM21 and ZR2016FM13.

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Zhang, X., Jian, M., Sun, Y. et al. Improving image segmentation based on patch-weighted distance and fuzzy clustering. Multimed Tools Appl 79, 633–657 (2020). https://doi.org/10.1007/s11042-019-08041-x

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  • DOI: https://doi.org/10.1007/s11042-019-08041-x

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