Many types of medical images must be fused, as single‐modality medical images can only provide limited information due to the imaging principles and the complexity of human organ structures. In this paper, a multimodal medical image fusion method that combines the advantages of nonsubsampling contourlet transform (NSCT) and fuzzy entropy is proposed to provide a basis for clinical diagnosis and improve the accuracy of target recognition and the quality of fused images. An image is initially decomposed into low‐ and high‐frequency subbands through NSCT. The corresponding fusion rules are adopted in accordance with the different characteristics of the low‐ and high‐frequency components. The membership degree of low‐frequency coefficients is calculated. The fuzzy entropy is also computed and subsequently used to guide the fusion of coefficients to preserve image details. High‐frequency components are fused by maximizing the regional energy. The final fused image is obtained by inverse transformation. Experimental results show that the proposed method achieves good fusion effect based on the subjective visual effect and objective evaluation criteria. This method can also obtain high average gradient, SD, and edge preservation and effectively retain the details of the fused image. The results of the proposed algorithm can provide effective reference for doctors to assess patient condition.

中文翻译：

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基于模糊熵和非下采样contourlet变换的医学图像融合方法的改进

必须融合多种类型的医学图像，因为由于成像原理和人体器官结构的复杂性，单模态医学图像只能提供有限的信息。本文提出了一种融合了非下采样轮廓波变换（NSCT）和模糊熵的优点的多模式医学图像融合方法，为临床诊断，提高目标识别的准确性和融合图像的质量提供了依据。图像最初通过NSCT分解为低频和高频子带。根据低频和高频分量的不同特性，采用相应的融合规则。计算低频系数的隶属度。模糊熵也被计算，并且随后被用于指导系数的融合以保留图像细节。通过最大化区域能量来融合高频分量。通过逆变换获得最终的融合图像。实验结果表明，该方法基于主观视觉效果和客观评价标准，取得了较好的融合效果。该方法还可以获得较高的平均梯度，SD和边缘保留，并有效保留融合图像的细节。所提算法的结果可为医生评估病情提供有效参考。实验结果表明，该方法基于主观视觉效果和客观评价标准，取得了较好的融合效果。该方法还可以获得高的平均梯度，SD和边缘保留，并有效保留融合图像的细节。所提算法的结果可为医生评估病情提供有效参考。实验结果表明，该方法基于主观视觉效果和客观评价标准，取得了较好的融合效果。该方法还可以获得高的平均梯度，SD和边缘保留，并有效保留融合图像的细节。所提算法的结果可为医生评估病情提供有效参考。