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A novel multi-modal medical image fusion algorithm

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Abstract

In order to improve the contrast of image fusion and highlight the unique characteristics of medical images, a multi-modal medical image fusion algorithm in the framework of non-subsampled contourlet transform (NSCT) is proposed in this paper. Firstly, the computed tomography images and magnetic resonance image are decomposed into low- and high-frequency sub-bands through the NSCT of multi-scale geometric transformation; secondly, for the low-frequency sub-band, the local area standard deviation method is selected or the fusion, while for the high-frequency sub-band, an adaptive pulse coupling neural network model is constructed and the fusion rules are set by the cumulative ignition times of iterative operation in the network; finally, the fusion image is obtained through image reconstruction. Experimental results show that the fusion results of the algorithm in this paper can improve the image fusion quality significantly and it has certain advantages in both visual effects and objective evaluation indexes, which provides a more reliable basis for clinical diagnosis and treatment of diseases.

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Acknowledgements

This work was supported by Project of the Shandong Medicine and Health Science Technology Development Plan under Grant 2017WSB04071, Shandong Province Science and Technology Development Plan Project under Grant 2014GSF118086.

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Authors and Affiliations

  1. Network Information Center, Shandong Women’s University, Jinan, China

    Xinhua Li

  2. School of Data and Computer Science, Shandong Women’s University, Jinan, China

    Jing Zhao

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  1. Xinhua Li
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  2. Jing Zhao
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Correspondence to Jing Zhao.

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Li, X., Zhao, J. A novel multi-modal medical image fusion algorithm. J Ambient Intell Human Comput 12, 1995–2002 (2021). https://doi.org/10.1007/s12652-020-02293-4

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  • DOI: https://doi.org/10.1007/s12652-020-02293-4

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