Segmentation of medical image based on mean shift and deterministic annealing EM algorithm
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作者:
Lee, Myung-Eun
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机构:
Chonnam Natl Univ, Dept Comp Sci, Gwanju, South KoreaChonnam Natl Univ, Dept Comp Sci, Gwanju, South Korea
Lee, Myung-Eun
[1
]
Kim, Soo-Hyung
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机构:
Chonnam Natl Univ, Dept Comp Sci, Gwanju, South KoreaChonnam Natl Univ, Dept Comp Sci, Gwanju, South Korea
Kim, Soo-Hyung
[1
]
Cho, Wan-Hyun
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机构:
Chonnam Natl Univ, Dept Stat, Yongdong, South KoreaChonnam Natl Univ, Dept Comp Sci, Gwanju, South Korea
Cho, Wan-Hyun
[2
]
Zhao, Xin
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机构:
Chonnam Natl Univ, Dept Comp Sci, Gwanju, South KoreaChonnam Natl Univ, Dept Comp Sci, Gwanju, South Korea
Zhao, Xin
[1
]
机构:
[1] Chonnam Natl Univ, Dept Comp Sci, Gwanju, South Korea
[2] Chonnam Natl Univ, Dept Stat, Yongdong, South Korea
来源:
2008 IEEE/ACS INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS, VOLS 1-3
|
2008年
关键词:
D O I:
10.1109/AICCSA.2008.4493653
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
In this paper, we use the mean shift procedure to determine the number of components in a mixture model and to detect their modes of each mixture component. Next, we have adopted the Gaussian mixture model to represent the probability distribution of feature vectors. A deterministic annealing expectation maximization algorithm is used to estimate the parameters of the GMM The experimental results show that the mean shift part of the proposed algorithm is efficient to determine the number of components and modes of each component in mixture models. And it shows that the DAEM part provides a global optimal solution for the parameter estimation in a mixture model.