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An efficient FCM-based method for image refinement segmentation

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Abstract

The conventional fuzzy c-means clustering (FCM) algorithm is sensitive to noise because no spatial information is taken into account. Many related algorithms reduce the influence of noise by adding local information to the objective function. However, there are still many problems, such as poor edge-preserving and anti-noise performance. This paper proposes an FCM-based method for image refinement segmentation to address the above problems effectively. We first take advantage of the pre-classification results of image sub-blocks as a new metric to measure the similarity of pixels and then combine the grayscale and spatial features of the local windows to vote and refine on these initial clustering results, which optimize the classification of pixels. Compared with existing algorithms, our algorithm can correct the misclassified pixels in the global segmentation and reserve image edge better. In addition, it is efficient for noisy image segmentation, which can maximize the recognition of noise and eliminate outliers. Experiments on both synthetic images and real-world images demonstrate the effectiveness and accuracy of the proposed method.

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Authors and Affiliations

  1. School of Software, Shandong University, Jinan, 250101, China

    Yueshuang Qi & Xuemei Li

  2. The Neurological Department of People’s Hospital of Lixia District of Jinan, Jinan, 250014, China

    Anxin Zhang

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

    Hua Wang

  4. Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, 264025, China

    Xuemei Li

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  1. Yueshuang Qi
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  2. Anxin Zhang
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  3. Hua Wang
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  4. Xuemei Li
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Correspondence to Xuemei Li.

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This work was supported partly by the National Natural Science Foundation of China under Grant Nos. 62072281, 62007017 and the Science and Technology Innovation Program for Distributed Young Talents of Shandong Province Higher Education Institutions under Grant No. 2019KJN042.

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Qi, Y., Zhang, A., Wang, H. et al. An efficient FCM-based method for image refinement segmentation. Vis Comput 38, 2499–2514 (2022). https://doi.org/10.1007/s00371-021-02126-1

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