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首页> 外文会议>Conference on Image Processing: Algorithms and Systems III; 20040119-20040121; San Jose,CA; US >Using Independent Component Analysis for Electrical Impedance Tomography
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Using Independent Component Analysis for Electrical Impedance Tomography

机译：使用独立分量分析进行电阻抗层析成像

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摘要

Independent component analysis (ICA) is a way to resolve signals into independent components based on the statistical characteristics of the signals. It is a method for factoring probability densities of measured signals into a set of densities that are as statistically independent as possible under the assumptions of a linear model. Electrical impedance tomography (EIT) is used to detect variations of the electric conductivity of the human body. Because there are variations of the conductivity distributions inside the body, EIT presents multi-channel data. In order to get all information contained in different location of tissue it is necessary to image the individual conductivity distribution. In this paper we consider to apply ICA to EIT on the signal subspace (individual conductivity distribution). Using ICA the signal subspace will then be decomposed into statistically independent components. The individual conductivity distribution can be reconstructed by the sensitivity theorem in this paper. Compute simulations show that the full information contained in the multi-conductivity distribution will be obtained by this method.
机译：独立分量分析（ICA）是一种根据信号的统计特征将信号分解为独立分量的方法。这是一种在线性模型的假设下将测量信号的概率密度分解为一组在统计上尽可能独立的密度的方法。电阻抗断层扫描（EIT）用于检测人体电导率的变化。由于人体内部的电导率分布存在变化，因此EIT会提供多通道数据。为了获得包含在组织的不同位置中的所有信息，必须对各个电导率分布成像。在本文中，我们考虑将ICA应用于信号子空间上的EIT（单个电导率分布）。然后使用ICA将信号子空间分解为统计上独立的组件。可以通过灵敏度定理来重建各个电导率分布。计算仿真表明，该方法将获得多电导率分布中包含的全部信息。

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