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- W2896916172 abstract "Abstract In this paper, we present a method which can generate color medical images based on multi-feature fusion and hierarchical density peak clustering. First, the proposed method extracts multi-feature to gain abundant feature information. Then, select the regional representative pixels utilizing the local density and neighborhood relationship of pixels. Next, color information is embedded to regional representative pixels. Finally, obtain the color information of each pixel according to the similarity of the representative pixel and non-representative pixel. The experiments on CT, MRI, PET, Ultrasound and DTI demonstrate that the color medical images generated by the proposed algorithm have exquisite details and clear texture information. Moreover, the complexity of the proposed method is O(MN), lower than O(N3) (DPDR). Compared with other corresponding methods, the proposed method has higher contrast, average gradient and lower information entropy." @default.
- W2896916172 created "2018-10-26" @default.
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- W2896916172 date "2019-02-01" @default.
- W2896916172 modified "2023-09-24" @default.
- W2896916172 title "Color perception algorithm of medical images using density peak based hierarchical clustering" @default.
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- W2896916172 doi "https://doi.org/10.1016/j.bspc.2018.09.013" @default.
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