Entropy is a measure of information in signals. Based on the conception of entropy, two new features-Shannon entropy and Threshold entropy are proposed in flow regime identification.

熵是信号序列信息量的表征，作者在对差压信号的分析中提出了两种基于熵概念的特征——香农熵和阈值熵。

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For the mutual information calculated by partial volume interpolation method and Shannon entropy, certain local extremums are inevitable, which may lead to inaccurate registration.

采用部分体积插值法和香农熵计算得到的互信息，无法避免会出现一些局部极值，可能导致错误的配准。

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Weighted entropy instead of Shannon entropy to compute the mutual information is proposed in this paper, and it has been used in medical image registration experiment.

提出了一种用加权熵代替香农熵的互信息计算方法，并将其应用于图像配准实验。

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This is the final entropy law from the fertile mind and mathematics of Claude Shannon of MIT and Bell Labs, who defined information as unexpected bits. (Predictable bits convey no information content, no entropy.) Information entropy is measured by its surprisal.

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