Multi‐modality medical image fusion (MMIF) procedures have been generally utilized in different clinical applications. MMIF can furnish an image with anatomical as well as physiological data for specialists that could advance the diagnostic procedures. Various models were proposed earlier related to MMIF though there is a need still exists to enhance the efficiency of the previous techniques. In this research, the authors proposed a novel fusion model based on optimal thresholding with deep learning concepts. An enhanced monarch butterfly optimization (EMBO) is utilized to decide the optimal threshold of fusion rules in shearlet transform. Then, low and high‐frequency sub‐bands were fused on the basis of feature maps and were given by the extraction part of the deep learning method. Here, restricted Boltzmann machine (RBM) was utilized to conduct the MMIF procedure. A benchmark dataset was utilized for training and testing purposes. The investigations were conducted utilizing a set of generally‐utilized pre‐enrolled CT and MR images that are publicly accessible. From the usage of fused low and high level frequency groups, the fused image can be attained. The simulation performance results were attained and the proposed model was proved to offer effective performance in terms of SD, edge quality (EQ), mutual information (MI), fusion factor (FF), entropy, correlation factor (CF), and spatial frequency (SF) with respective values being 97.78, 0.96, 5.71, 6.53, 7.43, 0.97, and 25.78 over the compared methods.

中文翻译：

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一种使用最优剪切波和深度学习的多模态医学图像融合新方法

多模态医学图像融合（MMIF）程序已普遍用于不同的临床应用。MMIF 可以为可以推进诊断程序的专家提供带有解剖和生理数据的图像。早先提出了与 MMIF 相关的各种模型，尽管仍然需要提高先前技术的效率。在这项研究中，作者提出了一种基于最优阈值和深度学习概念的新型融合模型。利用增强的帝王蝶优化（EMBO）来确定剪切波变换中融合规则的最佳阈值。然后，在特征图的基础上融合低频和高频子带，并由深度学习方法的提取部分给出。这里，受限玻尔兹曼机 (RBM) 用于进行 MMIF 程序。基准数据集用于训练和测试目的。这些调查是利用一组可公开访问的普遍使用的预登记 CT 和 MR 图像进行的。通过使用融合的低频和高频组，可以得到融合图像。获得了仿真性能结果，并证明所提出的模型在 SD、边缘质量 (EQ)、互信息 (MI)、融合因子 (FF)、熵、相关因子 (CF) 和空间频率方面具有有效的性能(SF) 与比较方法相比，各自的值为 97.78、0.96、5.71、6.53、7.43、0.97 和 25.78。这些调查是利用一组可公开访问的普遍使用的预登记 CT 和 MR 图像进行的。通过使用融合的低频和高频组，可以得到融合图像。获得了仿真性能结果，并证明所提出的模型在 SD、边缘质量 (EQ)、互信息 (MI)、融合因子 (FF)、熵、相关因子 (CF) 和空间频率方面具有有效的性能(SF) 与比较方法相比，各自的值为 97.78、0.96、5.71、6.53、7.43、0.97 和 25.78。这些调查是利用一组可公开访问的普遍使用的预登记 CT 和 MR 图像进行的。通过使用融合的低频和高频组，可以得到融合图像。获得了仿真性能结果，并证明所提出的模型在 SD、边缘质量 (EQ)、互信息 (MI)、融合因子 (FF)、熵、相关因子 (CF) 和空间频率方面具有有效的性能(SF) 与比较方法相比，各自的值为 97.78、0.96、5.71、6.53、7.43、0.97 和 25.78。