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Multimodal medical image fusion by cloud model theory

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Abstract

Image fusion can provide more extensive information since it combines two or more different images. Cloud model is a recently proposed theory in artificial intelligence and has the advantage of taking the randomness and fuzziness into account. In this paper, we introduce a novel multimodal medical image fusion method by cloud model theory. The proposed method fits the histograms of input images using the high-order spline function firstly and then divides intervals in line with the valley point of the fitted curve. On this basis, cloud models are generated adaptively through the reverse cloud generator. Finally, cloud reasoning rules are designed to achieve the fused image. Experimental results demonstrate that the fused images by proposed method show more image details and lesion regions than existing methods. The objective image quality assessment metrics on the fused images also show the superiority of the proposed method.

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Acknowledgements

This work is supported in part by Natural Science Foundation of China (Nos. 61572092, U1401252) and National Science & Technology Major Project (2016YFC1000307-3). The authors would like to thank the anonymous referees for their valuable comments and suggestions.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Weisheng Li, Jia Zhao & Bin Xiao

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  1. Weisheng Li
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  2. Jia Zhao
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  3. Bin Xiao
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Correspondence to Bin Xiao.

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Li, W., Zhao, J. & Xiao, B. Multimodal medical image fusion by cloud model theory. SIViP 12, 437–444 (2018). https://doi.org/10.1007/s11760-017-1176-6

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  • DOI: https://doi.org/10.1007/s11760-017-1176-6

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