Medical image fusion is an important technique to address the limited depth of the optical lens for a completely informative focused image. It can well improve the accuracy of diagnosis and assessment of medical problems. However, the difficulty of many traditional fusion methods in preserving all the significant features of the source images compromises the clinical accuracy of medical problems. Thus, we propose a novel medical image fusion method with a low-level feature to deal with the problem. We decompose the source images into base layers and detail layers with local binary pattern operators for obtaining low-level features. The low-level features of the base and detail layers are applied to construct weight maps by using saliency detection. The weight map optimized by fast guided filtering guides the fusion of base and detail layers to maintain the spatial consistency between the source images and their corresponding layers. The recombination of the fused base and detail layers constructs the final fused image. The experimental results demonstrated that the proposed method achieved a state-of-the-art performance for multifocus images.

中文翻译：

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基于低级特征的医学图像融合

医学图像融合是解决光学镜头有限深度以获得完全信息聚焦图像的重要技术。它可以很好地提高诊断和评估医疗问题的准确性。然而，许多传统融合方法在保留源图像的所有重要特征方面的困难影响了医学问题的临床准确性。因此，我们提出了一种具有低级特征的新型医学图像融合方法来处理该问题。我们使用局部二元模式算子将源图像分解为基础层和细节层，以获得低级特征。通过使用显着性检测，将基础层和细节层的低级特征应用于构建权重图。通过快速引导过滤优化的权重图引导基础层和细节层的融合，以保持源图像与其对应层之间的空间一致性。融合的基础层和细节层的重组构成了最终的融合图像。实验结果表明，所提出的方法在多焦点图像方面取得了最先进的性能。