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Proceedings Paper

Medical image fusion using pulse coupled neural network and multi-objective particle swarm optimization
Author(s): Quan Wang; Dongming Zhou; Rencan Nie; Xin Jin; Kangjian He; Liyun Dou
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Paper 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.

Paper Details

Date Published: 29 August 2016
PDF: 6 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100334K (29 August 2016); doi: 10.1117/12.2245043
Show Author Affiliations
Quan Wang, Yunnan Univ. (China)
Dongming Zhou, Yunnan Univ. (China)
Rencan Nie, Yunnan Univ. (China)
Xin Jin, Yunnan Univ. (China)
Kangjian He, Yunnan Univ. (China)
Liyun Dou, Yunnan Univ. (China)

Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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