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MR image segmentation based on level set method

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Abstract

In this paper, a level set model combining probabilistic statistics for image segmentation is proposed. Through adding a single-point pixel distribution into the energy function, the step size of each iteration is increased and the efficiency of the algorithm is improved. By adding the membership function of fuzzy clustering and bias field function, this method can effectively segment the image with intensity inhomogeneities. In addition, a new rule item is added to improve the edge segmentation effect of the image. Experiments on MR images of the brain show that the proposed model can provide ideal segmentation results compared with several level set segmentation models.

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Acknowledgments

This research was supported in part by the National Science Foundation of China (Grant No. 61101246) and the Fundamental Research Funds for the Central Universities (Grant No. JB180208).

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Authors and Affiliations

  1. School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Jin Liu, Xue Wei & Langlang Li

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  1. Jin Liu
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  2. Xue Wei
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  3. Langlang Li
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Correspondence to Jin Liu.

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Liu, J., Wei, X. & Li, L. MR image segmentation based on level set method. Multimed Tools Appl 79, 11487–11502 (2020). https://doi.org/10.1007/s11042-019-08468-2

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