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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| Published in: | Applied soft computing Vol. 46; pp. 588 - 595 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.09.2016
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| Subjects: | |
| ISSN: | 1568-4946, 1872-9681 |
| Online Access: | Get full text |
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| Abstract | [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. |
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| AbstractList | [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. |
| Author | Xu, Xinzheng Shan, Dong Wang, Guanying Jiang, Xiangying |
| Author_xml | – sequence: 1 givenname: Xinzheng orcidid: 0000-0001-6973-799X surname: Xu fullname: Xu, Xinzheng email: xxzheng@cumt.edu.cn, xuxinzh@163.com organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 2 givenname: Dong surname: Shan fullname: Shan, Dong organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 3 givenname: Guanying surname: Wang fullname: Wang, Guanying organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 4 givenname: Xiangying surname: Jiang fullname: Jiang, Xiangying organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China |
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| SubjectTerms | Multimodal medical image fusion Mutual information Pulse-coupled neural networks Quantum-behaved particle swarm optimization algorithm |
| Title | Multimodal medical image fusion using PCNN optimized by the QPSO algorithm |
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