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A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation

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Abstract

In medical field, it remains challenging to accurately segment medical images due to low contrast, complex noises and intensity inhomogeneity. To overcome these obstacles, this paper provides a novel edge-based active contour model (ACM) for medical image segmentation. Specifically, an accurate regularization approach is presented to maintain the level set function with a signed distance property, which guarantees the stability of the evolution curve and the accuracy of the numerical computation. More significantly, an adaptive perturbation is integrated into the framework of the edge-based ACM. The perturbation technique can balance the stability of curve evolution and the accuracy of segmentation, which is key for segmenting medical images with intensity inhomogeneity. A number of experiments on both artificial and real medical images demonstrate that the proposed segmentation model outperforms state-of-the-art methods in terms of robustness to noise and segmentation accuracy.

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Acknowledgments

We would like to thank all the anonymous reviewers for their valuable comments. This work is supported by the National Natural Science Foundation of China(Grant No. 61472289 and No. 61502356) and the National Key Research and Development Project(Grant No. 2016YFC0106305).

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  1. School of Computer Science, Wuhan University, Wuhan, China

    Haiping Yu & Yiteng Pan

  2. State Key Lab of Software Engineering, School of Computer Science, Wuhan University, Wuhan, China

    Fazhi He

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  1. Haiping Yu
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Yu, H., He, F. & Pan, Y. A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation. Multimed Tools Appl 78, 11779–11798 (2019). https://doi.org/10.1007/s11042-018-6735-5

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