含局部空间约束的t分布混合模型的点集配准

周志勇; 李莉华; 郑健; 蒯多杰; 胡粟; 张涛

doi:10.3724/SP.J.1004.2014.00683

含局部空间约束的t分布混合模型的点集配准

doi: 10.3724/SP.J.1004.2014.00683

1.
中国科学院苏州生物医学工程技术研究所医学影像室苏州 215163;
2.
重庆市电子工程职业学院计算机学院重庆 401331;
3.
苏州大学附属第一医院苏州 215006;
4.
中国科学院长春光学精密机械与物理研究所长春 130033

基金项目:

国家自然科学基金（61301042，61201117），江苏省自然科学基金（BK2012189）资助

详细信息

作者简介:
周志勇中国科学院苏州生物医学工程技术研究所助理研究员.主要研究方向为医学图像配准和处理方面的研究.E-mail：zhouzhiyong1638@163.com

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出版历程
- 收稿日期: 2012-06-28
- 修回日期: 2013-01-06
- 刊出日期: 2014-04-20

Point Sets Non-rigid Registration Using Student’s-t Mixture Model with Spatial Constraints

1.
Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163;
2.
School of Computer Science and Technology, Chongqing College of Electronic Engineering, Chongqing 401331;
3.
Suzhou First People's Hospital, The First Affiliated Hospital of Soochow University, Suzhou 215006;
4.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033

Funds:

Supported by National Natural Science Foundation of China (61301042, 61201117), and Natural Science Foundation of Jiangsu Province (BK2012189)

摘要

摘要: 基于高斯混合模型（Gaussian mixture model，GMM）的点集非刚性配准算法易受重尾点和异常点影响，提出含局部空间约束的t分布混合模型的点集非刚性配准算法. 通过期望最大化（Expectation maximization，EM）框架将高斯混合模型推广为t分布混合模型；把Dirichlet分布作为浮动点的先验权重，并构造含局部空间约束性质的Dirichlet 分布参数. 使用EM算法获得配准参数的闭合解；计算浮动点的自由度，改变其概率密度分布，避免异常点水平估计误差. 实验表明，本文提出的配准算法具有配准误差小、鲁棒性好、抗干扰能力强等优点.
- t分布混合模型 /
- Dirichlet分布 /
- 点集 /
- 非刚性配准 /
- 期望最大化算法
Abstract: A robust non-rigid registration framework using the student's-t mixture model with spatial constraints is proposed in this paper. The Gaussian mixture model which is vulnerable to outliers and data longer than normal tails is a special case of the student's-t mixture model in theory. The Dirichlet distribution is used as a prior distribution to reduce the impact of outliers. The Dirichlet parameter set with spatial constrains is structured to incorporate the spatial information into the decision process. The closed form solution of the parameter set of the student's-t mixture model is solved by re-parameterizing the student's-t mixture model in the expectation maximization (EM) algorithm. The degree of freedom of each moving point is calculated to change the probability density to reduce the registration error. It can also avoid estimating the outlier level of data sets that may bring additional error. The experiments showed that this non-rigid registration algorithm has features of high-accuracy and good robustness compared to other point set registration approaches.
- Student’s-t mixture model /
- Dirichlet distribution /
- point set /
- non-rigid registration /
- expectation maximization (EM) algorithm

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参考文献(31)

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含局部空间约束的t分布混合模型的点集配准

doi: 10.3724/SP.J.1004.2014.00683

作者简介:
周志勇中国科学院苏州生物医学工程技术研究所助理研究员.主要研究方向为医学图像配准和处理方面的研究.E-mail：zhouzhiyong1638@163.com

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Point Sets Non-rigid Registration Using Student’s-t Mixture Model with Spatial Constraints

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含局部空间约束的t分布混合模型的点集配准

doi: 10.3724/SP.J.1004.2014.00683

作者简介: 周志勇 中国科学院苏州生物医学工程技术研究所助理研究员.主要研究方向为医学图像配准和处理方面的研究.E-mail：zhouzhiyong1638@163.com

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出版历程

Point Sets Non-rigid Registration Using Student’s-t Mixture Model with Spatial Constraints

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出版历程

目录

作者简介:
周志勇中国科学院苏州生物医学工程技术研究所助理研究员.主要研究方向为医学图像配准和处理方面的研究.E-mail：zhouzhiyong1638@163.com