Abstract
Medical image segmentation is a complex study due to its disadvantages such as noise, low-contrast, intensity inhomogeneity, and so on. A novel level set model was proposed in this study to segment medical images accurately. The kernel function used to determine the size of neighborhood of central pixel was modified by Laplace kernel function, which is insensitive to the choice of parameters and is more suitable for segmenting medical images. Compared with several state-of-the-art models, both visual and objective experiments can demonstrate the performance and superiority of the novel level set model.
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References
Khadidos A, Li CT, Sanchez V (2017) Weighted level set evolution based on local edge features for medical image segmentation. IEEE Trans Image Process 26(4):1979–1991. https://doi.org/10.1109/TIP.2017.2666042
Fedkiw R, Gibou F, Osher S (2018) A review of level-set methods and some recent applications. J Comput Phys 385:82–109. https://doi.org/10.1016/j.jcp.2017.10.006
Osher S, Sethian JA (1988) Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys 79(1):12–49. https://doi.org/10.1016/0021-9991(88)90002-2
He L, Li CM, Mishra A et al (2009) Active contours driven by local Gaussian distribution fitting energy. Signal Process 89(12):2435–2447. https://doi.org/10.1016/j.sigpro.2009.03.014
Chang S, Chui CK, Li BN et al (2011) Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Comput Biol Med 41(1):1–10. https://doi.org/10.1016/j.compbiomed.2010.10.007
Ding ZH, Huang R, Gatenby JC et al (2011) A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process 20(7):2007–2016. https://doi.org/10.1109/TIP.2011.2146190
Song JH, Zhang Z (2019) An adaptive fuzzy level set model with local spatial information for medical image segmentation and bias correction. IEEE Access 7:27322–27338. https://doi.org/10.1109/ACCESS.2019.2900089
Acknowledgements
This study was supported in part by the Postgraduate Innovative Research Project of Heilongjiang University (Grand No. YJSCX2019-058HLJU), in part by the Research Project on Education and Teaching Reform of Minnan Normal University (Grant No. JG201920), and in part by the Fujian Provincial Natural Science Foundation Project (Grant No. 2017J01708).
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Song, J., Zhang, Z., Zhen, J. (2020). Medical Images Segmentation Using a Novel Level Set Model with Laplace Kernel Function. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_139
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DOI: https://doi.org/10.1007/978-981-13-9409-6_139
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