29 August 2016 Medical image fusion using pulse coupled neural network and multi-objective particle swarm optimization
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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100334K (2016) https://doi.org/10.1117/12.2245043
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
Abstract
Medical image fusion plays an important role in biomedical research and clinical diagnosis. In this paper, an efficient medical image fusion approach is presented based on pulse coupled neural network (PCNN) combining multi-objective particle swarm optimization (MOPSO), which solves the problem of PCNN parameters setting. Selecting mutual information (MI) and image quality factor (QAB/F) as the fitness function of MOPSO, the parameters of PCNN are adaptively set by the popular MOPSO algorithm. Computed tomography (CT) and magnetic resonance imaging (MRI) are the source images as experimental images. Compared with other methods, the experimental results show the superior processing performances in both subjective and objective assessment criteria.
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Quan Wang, Quan Wang, Dongming Zhou, Dongming Zhou, Rencan Nie, Rencan Nie, Xin Jin, Xin Jin, Kangjian He, Kangjian He, Liyun Dou, Liyun Dou, } "Medical image fusion using pulse coupled neural network and multi-objective particle swarm optimization", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100334K (29 August 2016); doi: 10.1117/12.2245043; https://doi.org/10.1117/12.2245043
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