Abstract
Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images’ information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images’ information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.
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Acknowledgements
This work was supported by Promotive Research Fund for Excellent Young and Middle-Aged Scientists of Shandong (BS2014DX012), China Postdoctoral Science Foundation (2015M581203), and Natural Science Foundation of Shandong Province (Grant ZR2014FQ026), State Key Laboratory of Coal Resources and Safe Mining (China University of Mining and Technology) Open Foundation (SKLCRSM16KFD05).
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Zheng, Q., Li, H., Fan, B. et al. Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images. Opt Rev 24, 653–659 (2017). https://doi.org/10.1007/s10043-017-0362-7
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DOI: https://doi.org/10.1007/s10043-017-0362-7