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Level Set Formulation Based on Edge and Region Information with Application to Accurate Lesion Segmentation of Brain Magnetic Resonance Images

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  • Yunyun Yang

    (Harbin Institute of Technology)

  • Wenjing Jia

    (Harbin Institute of Technology)

  • Xiu Shu

    (Harbin Institute of Technology)

  • Boying Wu

    (Harbin Institute of Technology)

Abstract

Magnetic resonance images have great significance for doctors’ analysis and diagnosis of diseases. One difficulty in segmenting magnetic resonance images is associated with the intensity inhomogeneity. In this paper, we propose an improved active contour model combining local and global information dynamically to segment images with intensity inhomogeneity. Besides, the atlas term is added into our energy functional, which improves the segmentation accuracy by restricting the segmented range around the location of the given atlas and making the contour move toward a position near the atlas. In this paper, we first present the multi-phase formulation of our model. Then, our model is applied to segment a total of 35 different brain magnetic resonance images with lesions. We also compare the performance of our model with other models, which can handle inhomogeneous images to some extent. Experimental results demonstrate that our model has promising performance for these challenging brain magnetic resonance images. Accuracy, efficiency and robustness of the proposed model have also been demonstrated by the numerical results and comparisons with other models.

Suggested Citation

  • Yunyun Yang & Wenjing Jia & Xiu Shu & Boying Wu, 2019. "Level Set Formulation Based on Edge and Region Information with Application to Accurate Lesion Segmentation of Brain Magnetic Resonance Images," Journal of Optimization Theory and Applications, Springer, vol. 182(2), pages 797-815, August.
    • Handle: RePEc:spr:joptap:v:182:y:2019:i:2:d:10.1007_s10957-018-01451-1
    DOI: 10.1007/s10957-018-01451-1
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    References listed on IDEAS

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    1. Yunyun Yang & Boying Wu, 2012. "Convex Image Segmentation Model Based on Local and Global Intensity Fitting Energy and Split Bregman Method," Journal of Applied Mathematics, Hindawi, vol. 2012, pages 1-16, March.
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