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Medical Image Segmentation Based on Spatially Constrained Inverted Beta-Liouville Mixture Models

  • Wenmin Chen
  • Wentao FanEmail author
  • Nizar Bouguila
  • Bineng Zhong
Chapter
Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)

Abstract

In this chapter, we propose an image segmentation method based on a spatially constrained inverted Beta-Liouville (IBL) mixture model for segmenting medical images. Our method adopts the IBL distribution as the basic distribution, which can demonstrate better performance than commonly used distributions (such as Gaussian distribution) in image segmentation. To improve the robustness of our image segmentation method against noise, the spatial relationship among nearby pixels is imposed into our model by using generalized means. We develop a variational Bayes inference algorithm to learn the proposed model, such that model parameters can be efficiently estimated in closed form. In our experiments, we use both simulated and real brain magnetic resonance imaging (MRI) data to validate our model.

Keywords

Medical image segmentation Inverted Beta-Liouville Spatially constrained model Mixture models Variational inference 

Notes

Acknowledgements

The completion of this work was supported by the National Natural Science Foundation of China (61876068, 61572205), the Natural Science Foundation of Fujian Province (2018J01094), and the Promotion Program for young and middle-aged teacher in Science and Technology Research of Huaqiao University (ZQNPY510).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Wenmin Chen
    • 1
  • Wentao Fan
    • 1
    Email author
  • Nizar Bouguila
    • 2
  • Bineng Zhong
    • 1
  1. 1.Department of Computer Science and TechnologyHuaqiao UniversityXiamenChina
  2. 2.Concordia Institute for Information Systems EngineeringConcordia UniversityMontrealCanada

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