Medical image fusion is a synthesis of visual information present in any number of medical imaging inputs into a single fused image without any distortion or loss of detail. It enhances image quality by retaining specific features to improve the clinical applicability of medical imaging for treatment and evaluation of medical conditions. A big challenge in the processing of medical images is to incorporate the pathological features of the complement into one image. The fused image presents various challenges, such as existence of fusion artifacts, hardness of the base, comparison of medical image input, and computational cost. The techniques of hybrid multimodal medical image fusion (HMMIF) have been designed for pathologic studies, such as neurocysticercosis, degenerative and neoplastic diseases. Two domain algorithms based on HMMIF techniques have been developed in this research for various medical image fusion applications for MRI-SPECT, MRI-PET, and MRI-CT. NSCT is initially used in the proposed method to decompose the input images which give components of low and high frequency. The average fusion rule applies to NSCT components with low frequency. The NSCT high frequency components are fused by the law of full fusion. NSCTs high frequency is handled with directed image filtration scheme. The fused picture is obtained by taking inverse transformations from all frequency bands with the coefficients obtained from them. The methods suggested are contrasted with traditional approaches in the state of the art. Experimentation proves that the methods suggested are superior in terms of both qualitative and quantitative assessment. The fused images using proposed algorithms provide information useful for visualizing and understanding the diseases to the best of both sources’ modality.

中文翻译：

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具有深度引导滤波的智能多模态医学图像融合

医学图像融合是将任意数量的医学成像输入中存在的视觉信息合成为单个融合图像，而不会出现任何失真或细节丢失。它通过保留特定特征来提高图像质量，以提高医学成像在治疗和评估医学状况时的临床适用性。医学图像处理的一大挑战是将补体的病理特征合并到一张图像中。融合图像提出了各种挑战，例如融合伪影的存在、基底的硬度、医学图像输入的比较以及计算成本。混合多模态医学图像融合 (HMMIF) 技术已被设计用于病理研究，例如神经囊尾蚴病、退行性疾病和肿瘤疾病。本研究开发了两种基于 HMMIF 技术的域算法，用于 MRI-SPECT、MRI-PET 和 MRI-CT 的各种医学图像融合应用。NSCT 最初用于在所提出的方法中分解给出低频和高频分量的输入图像。平均融合规则适用于低频的 NSCT 分量。NSCT高频分量按照全融合定律融合。NSCT 的高频使用定向图像过滤方案进行处理。融合图像是通过对所有频带进行逆变换而获得的。所建议的方法与现有技术中的传统方法形成对比。实验证明，所建议的方法在定性和定量评估方面都是优越的。