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Medical image fusion based on DTNP systems and Laplacian pyramid

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Abstract

Dynamic threshold neural P systems (DTNP systems) are a theoretical computing model proposed in previous work. As a variant of spiking neural P (SNP) systems, DTNP systems have two mechanisms: spiking and dynamic threshold mechanisms. By considering local connections of neurons, we design two-dimensional DTNP systems with local topology. Based on the DTNP systems, we develop a novel fusion method based on Laplacian pyramid for medical images. In the decomposition layers of Laplacian pyramid, WLE and INSML features are combined as the input of DTNP systems, and its output is used as the control signal of fusion rules. The proposed fusion method is evaluated on seven pairs of benchmark medical images and is compared with eight baseline fusion methods. Experimental results demonstrate the advantage of the proposed fusion method for the fusion of medical images.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (No. 62076206 and No. 62176216), China.

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Authors and Affiliations

  1. School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China

    Siheng Mi, Li Zhang & Hong Peng

  2. School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, 610039, China

    Jun Wang

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  1. Siheng Mi
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  2. Li Zhang
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  3. Hong Peng
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  4. Jun Wang
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Correspondence to Hong Peng.

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Mi, S., Zhang, L., Peng, H. et al. Medical image fusion based on DTNP systems and Laplacian pyramid. J Membr Comput 3, 284–295 (2021). https://doi.org/10.1007/s41965-021-00087-x

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