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A nonlocal energy minimization approach to brain image segmentation with simultaneous bias field estimation and denoising

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Abstract

Image segmentation plays an important role in medical image analysis. The most widely used image segmentation algorithms, region-based methods that typically rely on the homogeneity of image intensities in the regions of interest, often fail to provide accurate segmentation results due to the existence of bias field, heavy noise and rich structures. In this paper, we incorporate nonlocal regularization mechanism in the coherent local intensity clustering formulation for brain image segmentation with simultaneously estimating bias field and denoising, specially preserving good structures. We define an energy functional with a local data fitting term, two nonlocal regularization terms for both image and membership functions, and a \(L_2\) image fidelity term. By minimizing the energy, we get good segmentation results with well preserved structures. Meanwhile, the bias estimation and noise reduction can also be achieved. Experiments performed on synthetic and clinical brain magnetic resonance imaging data and comparisons with other methods are given to demonstrate that by introducing the nonlocal regularization mechanism, we can get more regularized segmentation results.

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Acknowledgments

This work was in part supported by a national science and technology project during the twelfth five-year plan (No. 2012BAI10B04) and the National Natural Science Foundation of China under Grant No. 11101365.

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Authors and Affiliations

  1. College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, 310053, People’s Republic of China

    Zengsi Chen

  2. Center of Mathematical Science, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Jinwei Wang

  3. Department of Mathematics, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Dexing Kong

  4. School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, 310018, People’s Republic of China

    Fangfang Dong

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  1. Zengsi Chen
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  2. Jinwei Wang
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  3. Dexing Kong
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  4. Fangfang Dong
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Correspondence to Fangfang Dong.

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Chen, Z., Wang, J., Kong, D. et al. A nonlocal energy minimization approach to brain image segmentation with simultaneous bias field estimation and denoising. Machine Vision and Applications 25, 529–544 (2014). https://doi.org/10.1007/s00138-013-0546-5

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  • DOI: https://doi.org/10.1007/s00138-013-0546-5

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