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• W3165334317 startingPage "102788" @default.
• W3165334317 abstract "The fusion of vital imaging information has turned into a primary issue for biomedical applications. In this paper, a new multi-modality medical image fusion method is proposed in the shearlet domain. In the proposed algorithm, input images are decomposed using Non-subsampled shearlet transform (NSST) to get low and high frequencies components. A novel procedure to decompose and combine the base layers and detail layers using the local extrema (LE) approach is used and fused using a Co-occurrence filter (CoF) in low-frequency components. In high-frequency components, an edge-preserving image fusion strategy is performed using sum modified Laplacian (SML) to combine the high-frequency coefficients. The experimental outcomes and comparative analysis are performed over the Multi-modal medical image dataset using proposed and existing methods. It is exhibited through test outcomes and assessments that the proposed technique beats the cutting-edge fusion strategies concerning edge preservation in subjective and objective assessment criteria." @default.
• W3165334317 created "2021-06-07" @default.
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• W3165334317 date "2021-07-01" @default.
• W3165334317 modified "2023-10-18" @default.
• W3165334317 title "Multi-modal medical image fusion framework using co-occurrence filter and local extrema in NSST domain" @default.
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• W3165334317 doi "https://doi.org/10.1016/j.bspc.2021.102788" @default.
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