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首页> 外文会议>International Conference on Artificial Neural Networks(ICANN 2006) pt.2; 20060910-14; Athens(GR) >Exploring the Intrinsic Structure of Magnetic Resonance Spectra Tumor Data Based on Independent Component Analysis and Correlation Analysis
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Exploring the Intrinsic Structure of Magnetic Resonance Spectra Tumor Data Based on Independent Component Analysis and Correlation Analysis

机译：基于独立分量分析和相关分析的磁共振光谱肿瘤数据的本征结构

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摘要

Analysis on magnetic resonance spectra (MRS) data gives a deep insight into pathology of many types of tumors. In this paper, a new method based on independent component analysis (ICA) and correlation analysis is proposed for MRS tumour data structure analysis. First, independent components and their coefficients are derived by ICA. Those components are interpreted in terms of metabolites, which interrelate with each other in tissues. Then correlation analysis is performed to reveal the interrelationship on coefficient of ICs, where residue dependence of components of metabolites remains. The method was performed on MRS data of hepatic encephalopathy. Experimental results reveal the intrinsic data structure and describe the pathological interrelation between parts of the structure successfully.
机译：磁共振波谱（MRS）数据的分析提供了对多种类型肿瘤病理学的深入了解。本文提出了一种基于独立成分分析（ICA）和相关性分析的新方法，用于MRS肿瘤数据结构分析。首先，独立分量及其系数由ICA导出。这些成分是根据在组织中相互关联的代谢物来解释的。然后进行相关分析以揭示IC系数之间的相互关系，其中代谢物成分的残留依赖性仍然存在。该方法是根据肝性脑病的MRS数据进行的。实验结果揭示了固有的数据结构，并成功地描述了结构各部分之间的病理相互关系。

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