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A medical image fusion method based on two-layer decomposition and improved spatial frequency

机译:基于两层分解和改进空间频率的医学图像融合方法

摘要

#$%^&*AU2020100199A420200319.pdf#####Abstract: Multi-modal medical image fusion is widely used in clinical diagnosis and treatment. This present patent investigates a novel multi-modal medical image fusion method based on Latent Low-Rank Representation (LatLRR) and modified spatial frequency. Firstly, the LatLRR is utilized to learn a project matrix which used to extract salient features. Then, the project matrix L after learning is used to decompose the source image into base part and detail part. Finally, for the detail layer, the modified spatial frequency is used to fuse the detail layer, and then guided filter is used to preserve the edge of the fused detail layer. For the base part, the method based on Visual saliency mapping (VSM) is used to fuse the base part. The fused image is obtained by combine the fused detail part and the base part. In addition, compared with other state-of-the-art fusion methods, experimental results demonstrate that proposed method has better fusion performance both in subjective visual and objective evaluation.
机译:#$%^&* AU2020100199A420200319.pdf #####抽象:多模式医学图像融合被广泛应用于临床诊断和治疗。本专利研究了一种新颖的多模式医学图像融合方法基于潜在的低秩表示(LatLRR)和修改的空间频率。首先,利用LatLRR学习用于提取显着性的项目矩阵特征。然后,将学习后的项目矩阵L用于分解源图像分为基础部分和细节部分。最后,对于细节层,修改后的空间频率用于融合细节层,然后使用导向滤波器来保留融合细节层的边缘。作为基础部分,基于视觉显着性的方法映射(VSM)用于融合基础部分。通过合并获得融合图像融合的细节部分和基础部分。另外,与其他相比最先进的融合方法,实验结果表明该方法在主观视觉和客观方面都具有更好的融合性能评价。

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