ArticleComputational and mathematical methods in medicine2021
Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- An enforced block diagonal low-rank representation method for the classification of medical image patterns.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2022Article
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2 authors.
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Abstract
A multimodal medical image fusion algorithm based on multiple latent low-rank representation is proposed to improve imaging quality by solving fuzzy details and enhancing the display of lesions. Firstly, the proposed method decomposes the source image repeatedly using latent low-rank representation to obtain several saliency parts and one low-rank part. Secondly, the VGG-19 network identifies the low-rank part's features and generates the weight maps. Then, the fused low-rank part can be obtained by making the Hadamard product of the weight maps and the source images. Thirdly, the fused saliency parts can be obtained by selecting the max value. Finally, the fused saliency parts and low-rank part are superimposed to obtain the fused image. Experimental results show that the proposed method is superior to the traditional multimodal medical image fusion algorithms in the subjective evaluation and objective indexes.
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