Bayesian mixture models of variable dimension for image segmentation.

Ferreira-da-Silva AR

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Bayesian mixture models of variable dimension for image segmentation.

机译：用于图像分割的可变维数的贝叶斯混合模型。

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We present Bayesian methodologies and apply Markov chain sampling techniques for exploring normal mixture models with an unknown number of components in the context of magnetic resonance imaging (MRI) segmentation. The experiments show that by estimating the number of components using sample-based approaches based on variable dimension models the discriminating power of the estimated components is improved. Two different MCMC methods are compared to perform the segmentation of simulated magnetic resonance brain scans, the reversible jump MCMC model and the Dirichlet process (DP) mixture model. The preference given to the Dirichlet process mixture model is discussed.

机译：我们提出了贝叶斯方法，并应用了马尔可夫链采样技术来探索在磁共振成像（MRI）分割背景下具有未知数量组分的正常混合物模型。实验表明，通过使用基于变量维模型的基于样本的方法来估计组件的数量，可以提高估计组件的识别能力。比较了两种不同的MCMC方法以执行模拟磁共振脑扫描的分割，即可逆跳MCMC模型和Dirichlet过程（DP）混合模型。讨论了对Dirichlet过程混合模型的偏好。

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《Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice》 |2009年第1期|共14页
作者
Ferreira-da-Silva AR;
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正文语种 eng
中图分类计算机的应用;
关键词
Bayes Theorem; Brain; Humans; Magnetic Resonance Imaging; Markov Chains; 贝叶斯定理; 脑; 磁共振成像; Markov链; 模型; 理论; 蒙特卡罗法; 体模; 显象术;

机译：Bayes Theorem;Brain;Humans;Magnetic Resonance Imaging;Markov Chains;贝叶斯定理;脑;磁共振成像;Markov链;模型;理论;蒙特卡罗法;体模;显象术;

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1. Bayesian mixture models of variable dimension for image segmentation. [J] . Ferreira-da-Silva AR Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice . 2009,第1期

机译：用于图像分割的可变维数的贝叶斯混合模型。
2. Structured Bayesian Gaussian process latent variable model: Applications to data-driven dimensionality reduction and high-dimensional inversion [J] . Atkinson Steven, Zabaras Nicholas Journal of Computational Physics . 2019,第期

机译：结构化贝叶斯高斯过程潜在变量模型：应用于数据驱动的维度降低和高维反转
3. Bayesian mixture models for complex high dimensional count data in phage display experiments [J] . Yuan Ji, Guosheng Yin, Kam-Wah Tsui, Journal of the Royal Statistical Society. Series C, Applied statistics . 2007,第Pt2期

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4. High Dimensional Non-linear Modeling with Bayesian Mixture of CCA [C] . Tikara Hosino International conference on neural information processing;ICONIP 2010 . 2011

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5. Bayesian Networks and Gaussian Mixture Models in Multi-Dimensional Data Analysis with Application to Religion-Conflict Data. [D] . Liu, Hui. 2012

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6. Are Negative Symptoms Dimensional or Categorical? Detection and Validation of Deficit Schizophrenia With Taxometric and Latent Variable Mixture Models [O] . Anthony O. Ahmed, Gregory P. Strauss, Robert W. Buchanan, 2015

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7. Some improvements to bayesian image segmentation. Part one : Modelling [O] . DESCOMBES (X.), MORRIS (R.), ZERUBIA (J.) 1997

机译：贝叶斯图像分割的一些改进。第一部分：建模
8. Application of the Conditional Population-Mixture Model to Image Segmentation. [R] . Sclove, S. L. 1982

机译：条件种群 - 混合模型在图像分割中的应用。

Bayesian mixture models of variable dimension for image segmentation.

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