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• W2106927076 abstract "Wavelet transforms have emerged as a powerful tool in image fusion. However, the study and analysis of medical image fusion is still a challenging area of research. Therefore, in this paper, we propose a multiscale fusion of multimodal medical images in wavelet domain. Fusion of medical images has been performed at multiple scales varying from minimum to maximum level using maximum selection rule which provides more flexibility and choice to select the relevant fused images. The experimental analysis of the proposed method has been performed with several sets of medical images. Fusion results have been evaluated subjectively and objectively with existing state-of-the-art fusion methods which include several pyramid- and wavelet-transform-based fusion methods and principal component analysis (PCA) fusion method. The comparative analysis of the fusion results has been performed with edge strength (Q), mutual information (MI), entropy (E), standard deviation (SD), blind structural similarity index metric (BSSIM), spatial frequency (SF), and average gradient (AG) metrics. The combined subjective and objective evaluations of the proposed fusion method at multiple scales showed the effectiveness and goodness of the proposed approach." @default.
• W2106927076 created "2016-06-24" @default.
• W2106927076 creator A5042414040 @default.
• W2106927076 creator A5071333197 @default.
• W2106927076 date "2013-01-01" @default.
• W2106927076 modified "2023-10-16" @default.
• W2106927076 title "Multiscale Medical Image Fusion in Wavelet Domain" @default.
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• W2106927076 doi "https://doi.org/10.1155/2013/521034" @default.
• W2106927076 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3885318" @default.
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