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Pattern Analysis and Applications

, Volume 22, Issue 1, pp 75–88 | Cite as

An image thresholding approach based on Gaussian mixture model

  • Like Zhao
  • Shunyi ZhengEmail author
  • Wenjing Yang
  • Haitao Wei
  • Xia Huang
Theoretical advances
  • 122 Downloads

Abstract

Image thresholding is an important technique for partitioning the image into foreground and background in image processing and analysis. It is difficult for traditional thresholding methods to get satisfactory performance on the noisy and uneven grayscale images. In this paper, we propose an image thresholding approach based on Gaussian mixture model (GMM) to solve this problem. GMM assumes that image is a mixture of two unknown parameters’ Gaussian distributions, which corresponds to foreground and background, respectively. Based on this assumption, we adopt expectation maximization algorithm with a simple initialization strategy to estimate the statistical parameters and utilize Bayesian criteria to generate the binary map. Furthermore, we calculate the posterior probabilities in consideration of neighborhood effect to achieve good performance on noisy and uneven grayscale images. Experimental results conducted on the synthetic and real images demonstrate the effectiveness of the proposed method.

Keywords

Image thresholding Gaussian mixture model EM algorithm Neighborhood information 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 41671452, 41701532), High-level talent Fund Project of Henan University of Technology (Grant No. 2018BS054) and the basic research funding of the Central University (Grant No. 2042016kf0012).

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Like Zhao
    • 1
    • 2
  • Shunyi Zheng
    • 2
    • 3
    Email author
  • Wenjing Yang
    • 2
  • Haitao Wei
    • 2
    • 4
  • Xia Huang
    • 2
    • 3
  1. 1.College of Information Science and EngineeringHenan University of TechnologyZhengzhouChina
  2. 2.School of Remote Sensing and Information EngineeringWuhan UniversityWuhan CityChina
  3. 3.Collaborative Innovation Center of Geospatial TechnologyWuhan CityChina
  4. 4.Institute of Smart CityZhengzhou UniversityZhengzhouChina

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