Multimodal medical image fusion technique is an important and essential coalescing technique for the medical images with different modalities. The fused medical image carries more useful information than two or more relevant individual medical images of modality. To enhance, preserve edge and feature information and to remove noise of the source images a novel medical image fusion method has been developed with multiscale edge preserving decomposition L₀ smoothing with sparse representation (SR) in nonsubsampled contourlet transform (NSCT) domain. The NSCT-based image fusion method provides richer information in the spatial and spectral domains simultaneously. In this method, initially, L₀ gradient smoothing filter is applied in two different modal clinical images separately to decompose the source images into two layers such as low frequency layer (LFL) and high frequency layer (HFL) which preserves the information and improves quality. To maintain the curve edges and the energy of the source medical images, the LFLs of different modal images are combined by using the NSCT - SR fusion rule also to protect the detailed information of input medical images and reduce redundant information, the HFLs are combined by max-absolute combination rule. By combining the reconstructed LFL and HFL, the resultant fused image is obtained. The experimental results show that the proposed work can provide better results than current methodologies in terms of both visual consistency and quantitative analysis.

中文翻译：

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使用稀疏表示的 L0 梯度平滑的多模态医学图像融合

多模态医学图像融合技术是不同模态医学图像的重要且必不可少的融合技术。融合的医学图像比两个或多个相关的个体医学图像携带更多有用的信息。为了增强、保留边缘和特征信息并去除源图像的噪声，已经开发了一种新的医学图像融合方法，该方法具有多尺度边缘保留分解 L ₀平滑和非下采样轮廓波变换 (NSCT) 域中的稀疏表示 (SR)。基于 NSCT 的图像融合方法同时在空间和光谱域中提供更丰富的信息。在这种方法中，最初，L ₀梯度平滑滤波器分别应用于两个不同的模态临床图像，将源图像分解为低频层（LFL）和高频层（HFL）两层，保留信息并提高质量。为了保持原始医学图像的曲线边缘和能量，使用NSCT-SR融合规则组合不同模态图像的LFLs，同时保护输入医学图像的详细信息并减少冗余信息，HFLs通过最大绝对组合规则。通过将重建的 LFL 和 HFL 结合起来，得到了合成的融合图像。实验结果表明，在视觉一致性和定量分析方面，所提出的工作可以提供比当前方法更好的结果。