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Active contours driven by non-local Gaussian distribution fitting energy for image segmentation

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Abstract

Image segmentation is still a challenging task in image processing field because of unpredictable noise and intensity inhomogeneity in images. In this paper, we present a novel active contour model for image segmentation by constructing a robust truncated kernel function. It utilizes image patches to perceive the neighborhood intensities of pixel at the same time considers the spatial distance within a local window. By using this truncated kernel function, the proposed method can accurately segment images with intensity inhomogeneity while guaranteeing certain noise robustness. Extensive evaluations on synthetic and real images are provided to demonstrate the superiority of our method. The model makes full use of image patch information to strengthen the robustness against noise and intensity inhomogeneity in images.

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Acknowledgments

This work has been partially supported by National Science Foundation of China (61371168) National High Technology Research and Development Program of China (No. 2013AA014604). Jiangsu Province Regular Institutions of Higher Learning Academic Degree Graduate Student Innovation Plan (KYZZ16_0192).

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Authors and Affiliations

  1. The School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China

    Yupeng Li, Guo Cao & Xuesong Li

  2. China United Network Communications Corporation, Jiangsu Branch, Nanjing, 210094, China

    Qian Yu

Authors
  1. Yupeng Li
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  2. Guo Cao
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  3. Qian Yu
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  4. Xuesong Li
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Correspondence to Guo Cao.

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Li, Y., Cao, G., Yu, Q. et al. Active contours driven by non-local Gaussian distribution fitting energy for image segmentation. Appl Intell 48, 4855–4870 (2018). https://doi.org/10.1007/s10489-018-1243-x

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  • DOI: https://doi.org/10.1007/s10489-018-1243-x

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