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Multimodal medical image fusion method based on structural functional cross neural network
Information Sciences | 更新时间：2024-02-01
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    • Multimodal medical image fusion method based on structural functional cross neural network

    • “针对多模态医学图像融合中存在的纹理细节模糊和对比度低的问题，一项创新性的研究取得了显著成果。研究团队提出了一种结构功能交叉神经网络的多模态医学图像融合方法，为医学影像分析领域带来了新的突破。该方法首先设计了结构功能交叉神经网络模型，有效提取了医学图像的结构和功能信息，实现了两种信息之间的交互，从而能够精准捕捉图像的纹理细节。其次，研究团队构建了一种新型注意力机制，通过动态调整结构信息和功能信息的权重，显著提升了融合图像的对比度和轮廓清晰度。最后，他们还设计了一个从融合图像到源图像的分解过程，确保了融合图像包含更丰富的细节信息。与近年来提出的七种高水平方法相比，该方法在AG、EN、SF、MI、QAB/F和CC等客观评价指标上平均提升了22.87%、19.64%、23.02%、12.70%、6.79%和30.35%。这一成果不仅展示了该方法在纹理细节和对比度方面的优势，还证明了其在主观视觉和客观指标上的优越性。这一研究的成功为医学影像分析领域带来了新的视角和解决方案，有望为医生提供更准确、更全面的诊断依据，助力医学领域实现更精准的诊断和治疗。”
      ”
    • Optics and Precision Engineering   Vol. 32, Issue 2, Pages: 252-267(2024)

    • DOI：10.37188/OPE.20243202.0252    

      CLC： TP391

    • Published：25 January 2024，

      Received：05 May 2023，

      Revised：13 July 2023，

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  • DI Jing,GUO Wenqing,REN Li,et al.Multimodal medical image fusion method based on structural functional cross neural network[J].Optics and Precision Engineering,2024,32(02):252-267. DOI： 10.37188/OPE.20243202.0252.

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