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Medical image fusion by independent component analysis

机译:通过独立成分分析进行医学图像融合

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摘要

A new approach is proposed for multimodality medical image fusion. The fusion scheme is achieved employing the independent component analysis (ICA). The idea is motivated by the factor that ICA can be used for taking into account higher-order statistical dependence component in the data. The advantage of this scheme is that the decomposition basis is determined by the image data alone and priori knowledge is not necessary. Therefore, ICA based techniques is different from those of wavelet and Bayesian's decision theory based method. The experiment results demonstrate the effectiveness of the fusion scheme.
机译:提出了一种新的多模态医学图像融合方法。融合方案是通过独立成分分析(ICA)实现的。这个想法是由ICA可以用来考虑数据中高阶统计依赖项的因素所激发的。该方案的优点在于,分解基础仅由图像数据确定,并且不需要先验知识。因此,基于ICA的技术不同于基于小波和贝叶斯决策理论的方法。实验结果证明了该融合方案的有效性。

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