The Gaussian Mixture Model Clustering of Medical Image Based on Initialization of Approximate Density Function

ZHANG Shu-wei; SONG Yu-qing; CHEN Jian-mei; XIE Cong-hua

Chinese

您当前的位置：

首页 >

文章列表页 >

The Gaussian Mixture Model Clustering of Medical Image Based on Initialization of Approximate Density Function

更新时间：2026-09-04

- The Gaussian Mixture Model Clustering of Medical Image Based on Initialization of Approximate Density Function
- Vol. 27, Issue 9, Pages: 168-171(2010)
- 作者机构：
  
  江苏大学机构计算机科学与通信工程学院,江苏,镇江,212013
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
移动端阅览
张树伟, 宋余庆, 陈健美, 谢从华. 基于近似密度初始化的医学图像混合模型聚类[J]. 微电子学与计算机, 2010, 27(9): 168-171.

ZHANG Shu-wei, SONG Yu-qing, CHEN Jian-mei, et al. The Gaussian Mixture Model Clustering of Medical Image Based on Initialization of Approximate Density Function[J]. 2010, 27(9): 168-171.
张树伟, 宋余庆, 陈健美, 谢从华. 基于近似密度初始化的医学图像混合模型聚类[J]. 微电子学与计算机, 2010, 27(9): 168-171. DOI：

ZHANG Shu-wei, SONG Yu-qing, CHEN Jian-mei, et al. The Gaussian Mixture Model Clustering of Medical Image Based on Initialization of Approximate Density Function[J]. 2010, 27(9): 168-171. DOI：

摘要

基于EM(ExpectationMaximization)的混合模型聚类的效果与参数的初始值存在密切的关系.提出了一种基于近似密度的EM参数初始化方法

该方法用近似密度估计聚类样本点

再根据每个聚类统计EM的混合比、均值、协方差参数的初始值.并应用于人体腹部医学图像数据的高斯混合模型聚类分析

实验结果表明该方法比Kmeans随机初始化方法有更好的聚类效果.

Abstract

The performance of EM algorithm heavily depends on the initial values of the parameters in EM.In this paper

The approximate density function is adopted to initialize EM.The method estimate samples by the approximate density function and statistics mixturerate

mean value

and covariance.The application of these parameters in analysis of Gaussian Mixture Desity Mode based on real human abdomen medical images and the results of experiments show that it can achieve better effect than Kmeans and random initialization.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on assembly action recognition based on MSS-YOLO-MPHands fusion architecture

Research on the design of blockchain-based AI-generated content traceability framework

High gain stability high temperature operational amplifier based on reverse body bias

Monte carlo analysis of V-layout trench SiC neutron detector

Steganography model guided by attention mechanism and improved dense convolution

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰