Application of Independent Component Analysis on PET Image De-noising

XU Qiu-ping; WEI Qi

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Application of Independent Component Analysis on PET Image De-noising

更新时间：2026-07-10

- Application of Independent Component Analysis on PET Image De-noising
- Vol. 14, Issue 6, Pages: 61-65(2009)
- 作者机构：
  
  哈尔滨理工大学电气与电子工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.41
- Published：2009
- 稿件说明：
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XU Qiu-ping, WEI Qi. Application of Independent Component Analysis on PET Image De-noising[J]. 2009, 14(6): 61-65.
DOI：

XU Qiu-ping, WEI Qi. Application of Independent Component Analysis on PET Image De-noising[J]. 2009, 14(6): 61-65. DOI：

摘要

独立分量分析(independent component analysis

ICA)是基于信号高阶统计量的盲源分离方法.提出将这种算法与中值滤波和小波滤波相结合应用于正电子发射计算机断层扫描(Positron Emission Tomography

PET)图像的去噪处理中

并与中值滤波和小波滤波进行对比分析.仿真实验证明了本文算法相较于中值滤波和小波滤波在PET图像处理中取得了更好的效果.

Abstract

Independent Component Analysis(ICA) is a blind source separation algorithm based on high statistics of singnal.Combined with median filter and wavelet filter

the algorithm was applied to PET image de-noising and compared with median filter and wavelet filter in this paper.Simulation experiments proved that the algorithm in the paper is better than median filter and wavelet filter in the process of PET image de-noising.

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