基于MDLatLRR的CT和MRI图像融合增强方法
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上海市自然科学基金资助项目(18ZR1426900)


CT and MRI image fusion enhancement method based on MDLatLRR
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    摘要:

    以往所提出的医学图像融合算法均对源图像提取相同分解层次的特征,忽略了源图像的特有特征。针对这一问题,提出一种根据不同模态医学图像提取其特有特征的融合方法。首先,使用改进的多级潜在低秩表示分解方法,在提取CT和MRI基础信息和细节信息的基础上,根据成像特点的不同,进一步提取CT图像的骨骼轮廓信息和MRI图像的软组织细节信息。然后,提出一种局部信息熵加权的区域能量函数方法融合细节信息,利用结构显著性度量和改进拉普拉斯能量和方法共同融合基础信息。最后,提出图像引导增强算法,以特有特征为引导对融合后的基础层和细节层进行增强。经实验证明,相比近几年具有代表性的融合方法,所提出的方法不仅在AG,EPI,VIF,SD客观评价指标中分别平均提高了9.45%,11.75%,14.79%,10.51%,而且在主观评价中也取得更好的效果,实现了CT和MRI图像精准融合。

    Abstract:

    The previously proposed medical image fusion algorithms extracted the same level of features from the source image, ignoring the unique features of the source image. To solve the problem, this work proposed a fusion method to extract unique features from different modal medical images. Firstly, the improved multi-level decomposition based latent low-rank representation method was used to extract the basic information and detailed information of CT and MRI images, and further extracted the bone contour information of CT image and soft tissue detail information of MRI image according to the different imaging principles. Then, this work proposed a local information entropy-weighted local energy function method to fuse the detail information, and utilized the structural saliency and sum of eight-neighborhood based modified laplacian to fuse the basic information. Finally, an image-guided enhancement method was proposed to enhance the fused base layer and detail layer with unique features as the guide. Experiments showed that, compared with the representative fusion methods of recent years, this approach not only improves the objective evaluation indicators of AG, EPI, VIF and SD by 9.45%, 11.75%, 14.79% and 10.51%, respectively, but also achieves better results in subjective evaluation, and realizes the accurate fusion of CT and MRI images.

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靳梦姣,王远军.基于MDLatLRR的CT和MRI图像融合增强方法[J].上海理工大学学报,2024,46(5):545-555.

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  • 收稿日期:2023-03-28
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  • 在线发布日期: 2024-10-29