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Open Access

Robust Segmentation Method for Noisy Images Based on an Unsupervised Denosing Filter

College of Software, Taiyuan University of Technology, Taiyuan 030024, China
College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
Information Technology Department, Shanxi Tizones Technology Co., Ltd, Taiyuan 030024, China
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Abstract

Level-set-based image segmentation has been widely used in unsupervised segmentation tasks. Researchers have recently alleviated the influence of image noise on segmentation results by introducing global or local statistics into existing models. Most existing methods are based on the assumption that the distribution of image noise is known or observable. However, real-time images do not meet this assumption. To bridge this gap, we propose a novel level-set-based segmentation method with an unsupervised denoising mechanism. First, a denoising filter is acquired under the unsupervised learning paradigm. Second, the denoising filter is integrated into the level-set framework to separate noise from the noisy image input. Finally, the level-set energy function is minimized to acquire segmentation contours. Extensive experiments demonstrate the robustness and effectiveness of the proposed method when applied to noisy images.

Keywords

image segmentationautoencodernoisy imagelevel set

References

[1]
Y. Wang, G. Yan, H. Zhu, S. Buch, and Z. Zhong. VC-Net: Deep volume-composition networks for segmentation and visualization of highly sparse and noisy image data, IEEE Transactions on Visualization and Computer Graphics, vol. 99, p. 1, 2020.
Google Scholar
[2]
S. J. Niu, Q. Chen, D. S. Luis, and L. R. Daniel, Robust noise region-based active contour model via local similarity factor for image segmentation, Pattern Recognition: The Journal of the Pattern Recognition Society, vol. 61, pp. 104–119, 2017.
Google Scholar
[3]
N. Tag, F. Zhou, Z. Gu, H. Zheng, Z. Yu, and B. Zheng, Unsupervised pixel-wise classification for chaetoceros image segmentation, Neurocomputing, vol. 318, no. 27, pp. 261–270, 2018.
Google Scholar
[4]
L. Deng, M. Yang, Z. Liang, Y. He, and C. Wang, Fusing geometrical and visual information via superpoints for the semantic segmentation of 3D road scenes, Tsinghua Science and Technology, vol. 25, no. 4, pp. 498–507, 2020.
Google Scholar
[5]
L. Guo, L. Chen, C. L. P. Chen, and J. Zhou, Integrating guided filter into fuzzy clustering for noisy image segmentation, Digital Signal Processing, vol. 83, pp. 235–248, 2018.
Google Scholar
[6]
G. Li, H. F. Li, and L. Zhang, Novel model using kernel function and local intensity information for noise image segmentation, Tsinghua Science and Technology, vol. 23, no. 3, pp. 303–314, 2018.
Google Scholar
[7]
J. Liu, M. Li, J. X. Wang, F. X. Wu, T. M. Liu, and P. Yi, A survey of MRI-based brain tumor segmentation methods, Tsinghua Science and Technology, vol. 19, no. 6, pp. 578–595, 2014.
Google Scholar
[8]
X. F. Wang, H. Min, L. Zou, and Y. G. Zhang, A novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement, Pattern Recognition, vol. 48, no. 1, pp. 189–204, 2015.
Google Scholar
[9]
L. Zhang, X. G. Peng, G. Li, and H. F. Li, A novel active contour model for image segmentation using local and global region-based information, Machine Vision and Applications, vol. 28. no. 1, pp. 75–89, 2017.
Google Scholar
[10]
T. Ivanovska, R. Laqua, L. Wang, A. Schenk, and J. H. Yoon, An efficient level set method for simultaneous intensity inhomogeneity correction and segmentation of MR images, Computerized Medical Imaging and Graphics, vol. 48, pp. 9–20, 2016.
Google Scholar
[11]
C. Li, R. Huang, Z. Ding, J. C. Gatenby, D. N. Metaxas, and J. C. Gore, A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI, IEEE Transactions on Image Processing, vol. 20, no. 7, pp. 2007–2016, 2011.
Google Scholar
[12]
J. Xu, Y. Huang, L. Liu, F. Zhu, and L. Shao, Noisy-as-clean: Learning unsupervised denoisingfrom the corrupted image, arXiv preprint arXiv: 1906. 06878, 2020.
Google Scholar
[13]
L. Wang and C. Pan, Robust level set image segmentation via a local correntropy-based K-means clustering, Pattern Recognition, vol. 47, no. 5, pp.1917–1925, 2013.
Google Scholar
[14]
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Nature, vol. 323, no. 6088, pp. 533–536, 1986.
Google Scholar
[15]
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. A. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Journal of Machine Learning Research, no. 11, pp. 3371–3408, 2010.
Google Scholar
[16]
Y. Lecun, L. Bottou, and Y. Bengio, Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324,1998.
Google Scholar
[17]
X. Xie, C. Wang, A. Zhang, and X. Meng, A robust level set method based on local statistical information for noisy image segmentation, Optik-International Journal for Light and Electron Optics, vol.125, no. 9, pp. 2199–2204, 2014.
Google Scholar
[18]
X. Chen, Y. Wang, and X. Wu, Local image intensity fitting model combining global image information, Computer Application, vol. 38, no.12, pp. 3574–3579, 2018.
Google Scholar
[19]
C. Li, C. Y. Kao, J. C. Gore, and Z. Ding, Implicit active contours driven by local binary fitting energy, presented at IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 2007.
Tsinghua Science and Technology
Volume 26 Issue 5,
October 2021
Pages 736-748
DOI: 10.26599/TST.2021.9010021
Cite this article:
Zhang L, Liu J, Shang F, et al. Robust Segmentation Method for Noisy Images Based on an Unsupervised Denosing Filter. Tsinghua Science and Technology, 2021, 26(5): 736-748. https://doi.org/10.26599/TST.2021.9010021

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Received: 27 February 2021
Accepted: 12 March 2021
Published: 20 April 2021
© The author(s) 2021

© The author(s) 2021. The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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