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Adaptive Energy Weight Based Active Contour Model for Robust Medical Image Segmentation

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Abstract

The performance of the traditional active contour model is subject to the energy weight parameters and initial level set functions, which significantly affect the accuracy of segmentation results. This paper proposes a new robust active contour model to eliminate the above limitations for both 2D single-object and multi-object medical image segmentation. The optimal values of energy weight parameters are defined with adaptive energy weight functions to adjust the contribution of each external energy term dynamically. Thus, the energy functional will not be controlled by the large external energy terms, otherwise boundaries leakage would occur. The initial level set functions are optimized with the coarse results obtained by fuzzy C-means clustering method. The evolution of level set functions starts from the locations near the true boundaries. Therefore, the deformable curves could converge to the true boundaries robustly. The proposed algorithm is verified using both synthetic images and medical images from different modalities. The experimental results demonstrate that the proposed algorithm could realize accurate and robust segmentation for medical images even in the presence of noises and weak boundaries.

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Acknowledgements

We kindly acknowledge the support of this study by the National Natural Science Foundation of China (No. 61472216), and the Ph. D. Programs Foundation of Ministry of Education of China (No. 20120002110067).

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Authors and Affiliations

  1. The State Key Laboratory of Precision Measurement Technology and Instrument, and the Department of Precision Instrument, Tsinghua University, Beijing, China

    Xuanping Li, Xue Wang & Yixiang Dai

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  1. Xuanping Li
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  2. Xue Wang
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  3. Yixiang Dai
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Correspondence to Xue Wang.

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Li, X., Wang, X. & Dai, Y. Adaptive Energy Weight Based Active Contour Model for Robust Medical Image Segmentation. J Sign Process Syst 90, 449–465 (2018). https://doi.org/10.1007/s11265-017-1257-3

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  • DOI: https://doi.org/10.1007/s11265-017-1257-3

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