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Two Fast and Robust Modified Gaussian Mixture Models Incorporating Local Spatial Information for Image Segmentation

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Abstract

The Gaussian Mixture Model (GMM) with the spatial constraint, e.g. Hidden Markov Random Field (HMRF), has been proven effective for image segmentation. The parameter β in the HMRF model is used to balance between robustness to noise and effectiveness of preserving the detail of the image. In other words, the determination of parameter β is, in fact, noise dependent to some degree. In this paper, we propose a simple and effective algorithm to make the traditional Gaussian Mixture Model more robust to noise, with consideration of the relationship between the local spatial information and the pixel intensity value information. The conditional probability of an image pixel is influenced by the probabilities of pixels in its immediate neighborhood to incorporate the spatial and the intensity information. In this case, the parameter β can be assigned to a small value to preserve image sharpness and detail in non-noise images. Meanwhile, the neighborhood window is used to tolerate the noise for heavy-noised images. Thus, the parameter β is independent of image noise degree in our model. Furthermore, we propose another algorithm for our modified GMM (MGMM) with the simplification of conditional probability computation (MGMM_S). Finally, our algorithm is not limited to GMM – it is general enough so that it can be applied to other distributions based on the construction of the Finite Mixture Model (FMM) technique. Experimental results of synthetic and real images demonstrate the improved robustness and effectiveness of our approach.

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Acknowledgments

This work was supported in part by the Canada Chair Research Program and the Natural Sciences and Engineering Research Council of Canada. This work was supported in part by the National Natural Science Foundation of China under Grant 61105007 and 61103141. This work was supported in part by PAPD (A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions). This work was supported in part by the Natural Science Foundation of Jiangsu Province NO. BK2012858. This work was supported in part by the Innovation Fund of China Academy of Space Technology: CAST201302.

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Authors and Affiliations

  1. School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, China

    Hui Zhang, Yuhui Zheng, Danhua Xu, Dingcheng Wang & Q. M. Jonathan Wu

  2. JiangSu Provincial Center for Disease Prevention and Control, Nanjing, China

    Tian Wen

  3. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, China

    Hui Zhang, Yuhui Zheng, Danhua Xu & Dingcheng Wang

  4. Department of Electrical and Computer Engineering, University of Windsor, Windsor, Canada

    Hui Zhang, Thanh Minh Nguyen & Q. M. Jonathan Wu

Authors
  1. Hui Zhang
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  2. Tian Wen
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  3. Yuhui Zheng
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  4. Danhua Xu
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  5. Dingcheng Wang
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  6. Thanh Minh Nguyen
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  7. Q. M. Jonathan Wu
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Correspondence to Q. M. Jonathan Wu.

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Hui Zhang and Tian Wen contributed equally to this work.

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Zhang, H., Wen, T., Zheng, Y. et al. Two Fast and Robust Modified Gaussian Mixture Models Incorporating Local Spatial Information for Image Segmentation. J Sign Process Syst 81, 45–58 (2015). https://doi.org/10.1007/s11265-014-0898-8

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  • DOI: https://doi.org/10.1007/s11265-014-0898-8

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