Medical image fusion based on NSST and PCNN optimized by PSO-DE

Ruihong Liu; Lixia Du; Chengxiang Liu

doi:10.1117/12.2635361

6 May 2022 Medical image fusion based on NSST and PCNN optimized by PSO-DE

Ruihong Liu, Lixia Du, Chengxiang Liu

Proceedings Volume 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022); 122560A (2022) https://doi.org/10.1117/12.2635361
Event: 2022 International Conference on Electronic Information Engineering, Big Data and Computer Technology, 2022, Sanya, China

Abstract

For the fusion of traditional medical image fusion effect is poor, a pseudo gibbs phenomenon is complex and PCNN parameters Settings and so on, proposed a based on the next sampling shear wave transform (NSST) and particle swarm optimization algorithm (PSO), the standard differential evolution algorithm (DE) combining optimization pulse coupled neural network (PCNN) parameters of medical image fusion method. Source image in NSST domain is decomposed into the same size high frequency sub-bands of k and a low frequency subband, combines PSO and DE using spatial frequency (SF) as the fitness function of the optimization algorithm to improve the PCNN, search for the optimal parameters for fusion of the high frequency subband coefficients, low-frequency subband coefficients of energy weighted average method is adopted to improve the fusion, Finally, NSST inverse transformation is used to obtain the final fusion image. The fusion effect of medical images was evaluated and analyzed according to subjective and objective evaluation indexes. Experimental results show that this algorithm is better than other algorithms in objective evaluation index and has better fusion effect.

Citation Download Citation

Ruihong Liu, Lixia Du, and Chengxiang Liu "Medical image fusion based on NSST and PCNN optimized by PSO-DE", Proc. SPIE 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022), 122560A (6 May 2022); https://doi.org/10.1117/12.2635361

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KEYWORDS

Image fusion

Medical imaging

Computed tomography

Magnetic resonance imaging

Particle swarm optimization

Particles

Tissues

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