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

  • Haiping Yu
  • Fazhi He
  • Yiteng Pan
Article
  • 56 Downloads

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.

Keywords

Intensity inhomogeneity Adaptive perturbation Medical image segmentation Computer vision 

Notes

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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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceWuhan UniversityWuhanChina
  2. 2.State Key Lab of Software Engineering, School of Computer ScienceWuhan UniversityWuhanChina

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