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• W3043104214 abstract "BACKGROUND: Multi-modal medical image fusion plays a crucial role in many areas of modern medicine like diagnosis and therapy planning. OBJECTIVE: Due to the factor that the structure tensor has the property of preserving the image geometry, we utilized it to construct the directional structure tensor and further proposed an improved 3-D medical image fusion method. METHOD: The local entropy metrics were used to construct the gradient weights of different source images, and the eigenvectors of traditional structure tensor were combined with the second-order derivatives of image to construct the directional structure tensor. In addition, the guided filtering was employed to obtain detail components of the source images and construct a fused gradient field with the enhanced detail. Finally, the fusion image was generated by solving the functional minimization problem. RESULTS AND CONCLUSION: Experimental results demonstrated that this new method is superior to the traditional structure tensor and multi-scale analysis in both visual effect and quantitative assessment." @default.
• W3043104214 created "2020-07-23" @default.
• W3043104214 creator A5077403058 @default.
• W3043104214 date "2020-09-19" @default.
• W3043104214 modified "2023-10-14" @default.
• W3043104214 title "Fusion of 3-D medical image gradient domain based on detail-driven and directional structure tensor" @default.
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• W3043104214 doi "https://doi.org/10.3233/xst-200684" @default.
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• W3043104214 hasPublicationYear "2020" @default.
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