Multimodal medical image fusion using PCNN optimized by the QPSO algorithm

[Display omitted] This paper proposed a method to fuse multimodal medical images using the adaptive pulse-coupled neural networks (PCNN), which was optimized by the quantum-behaved particle swarm optimization (QPSO) algorithm. In this fusion model, two source images, A and B, were first processed by...

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Vydáno v:Applied soft computing Ročník 46; s. 588 - 595
Hlavní autoři: Xu, Xinzheng, Shan, Dong, Wang, Guanying, Jiang, Xiangying
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.09.2016
Témata:
Multimodal medical image fusion
Mutual information
Pulse-coupled neural networks
Quantum-behaved particle swarm optimization algorithm
Pulse-coupled neural networks
Multimodal medical image fusion
Mutual information
Quantum-behaved particle swarm optimization algorithm
ISSN:1568-4946, 1872-9681
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Shrnutí:[Display omitted] This paper proposed a method to fuse multimodal medical images using the adaptive pulse-coupled neural networks (PCNN), which was optimized by the quantum-behaved particle swarm optimization (QPSO) algorithm. In this fusion model, two source images, A and B, were first processed by the QPSO-PCNN model, respectively. Through the QPSO algorithm, the PCNN model could find the optimal parameters for the source images, A and B. To improve the efficiency and quality of QPSO, three evaluation criteria, image entropy (EN), average gradient (AG) and spatial frequency (SF) were selected as the hybrid fitness function. Then, the output of the fusion model was obtained by the judgment factor according to the firing maps of two source images, which maybe was the pixel value of the image A, or that of the image B, or the tradeoff value of them. Based on the output of the fusion model, the fused image was gained. Finally, we used five pairs of multimodal medical images as experimental data to test and verify the proposed method. Furthermore, the mutual information (MI), structural similarity (SSIM), image entropy (EN), etc. were used to judge the performances of different methods. The experimental results illustrated that the proposed method exhibited better performances.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2016.03.028