A Robust Fuzzy Algorithm Based on Student's t-Distribution and Mean Template for Image Segmentation Application

@article{Zhang2013ARF,
  title={A Robust Fuzzy Algorithm Based on Student's t-Distribution and Mean Template for Image Segmentation Application},
  author={Hui Zhang and Q. Wu and T. Nguyen},
  journal={IEEE Signal Processing Letters},
  year={2013},
  volume={20},
  pages={117-120}
}
  • Hui Zhang, Q. Wu, T. Nguyen
  • Published 2013
  • Mathematics, Computer Science
  • IEEE Signal Processing Letters
  • Fuzzy c-means (FCM) with spatial constraints has been considered as an effective algorithm for image segmentation. Student's t-distribution has come to be regarded as an alternative to Gaussian distribution, as it is heavily tailed and more robust for outliers. In this letter, we propose a new algorithm to incorporate the merits of these two approaches. The advantages of our method are as follows: First, we incorporate the local spatial information and pixel intensity value by considering the… CONTINUE READING
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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 17 REFERENCES
    A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation
    174
    A Robust Fuzzy Local Information C-Means Clustering Algorithm
    699
    A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data
    1611
    Robust Student's-t Mixture Model With Spatial Constraints and Its Application in Medical Image Segmentation
    96
    A Measure for Objective Evaluation of Image Segmentation Algorithms
    238
    A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
    4901
    A spatially constrained mixture model for image segmentation
    216
    New Fuzzy Texture Features for Robust Detection of Moving Objects
    28