Fast Image Segmentation Model Combined with Fuzzy C-means Method and Curve Evolution

MA Yingran; PENG Yanjun

doi:10.11999/JEIT160786

Volume 39 Issue 6

Jun. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(6): 1379-1386

MA Yingran, PENG Yanjun. Fast Image Segmentation Model Combined with Fuzzy C-means Method and Curve Evolution[J]. Journal of Electronics & Information Technology, 2017, 39(6): 1379-1386. doi: 10.11999/JEIT160786

Citation:

MA Yingran, PENG Yanjun. Fast Image Segmentation Model Combined with Fuzzy C-means Method and Curve Evolution[J]. Journal of Electronics & Information Technology, 2017, 39(6): 1379-1386. doi: 10.11999/JEIT160786

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Fast Image Segmentation Model Combined with Fuzzy C-means Method and Curve Evolution

doi: 10.11999/JEIT160786

MA Yingran¹,
PENG Yanjun¹

Funds:

The National Natural Science Foundation of China (61502279), The National Key Research and Development Projects (2016YFC0801406), Shandong Provincial Natural Science Foundation (ZR2015FM013), Shandong Province Key Research and Development Projects (2016GSF120012), Taishan Scholar Project

Received Date: 2016-07-22
Rev Recd Date: 2017-02-09
Publish Date: 2017-06-19

Abstract

Abstract

To solve the problems about noise sensitivity and unclosed segmentation object boundaries in Fuzzy C-means Method (FCM), this paper proposes a fast image segmentation model combined with FCM and curve evolution, based on the pseudo level set formulations and object boundary curves, which are defined on the membership matrixes of FCM. To get the smooth and closed segmentation object boundaries, the Gaussian filter is performed on the pseudo level sets to approximate the function of the curve length regularization term. To eliminate the influence of Gaussian filter on the results of FCM, the gray values of the noisy points?are corrected, according to a new introduced edge-stop function and the mapping relationship between the gray value and membership degree. The FCM and the smoothing object boundary stage are performed alternately, which improves the robustness of this model. The experimental results show that the proposed model can overcome the influence of noise and get better segmentation results.
- Image segmentation,
- Fuzzy C-Means (FCM),
- Active contour,
- Curve evolution

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References(22)

References

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