Medical Image Fusion Using Pulse Coupled Neural Network and Multi-objective Particle Swarm Optimization

被引：4

作者：

Wang, Quan ^{[1
]}

Zhou, Dongming ^{[1
]}

Nie, Rencan ^{[1
]}

Jin, Xin ^{[1
]}

He, Kangjian ^{[1
]}

Dou, Liyun ^{[1
]}

机构：

[1] Yunnan Univ, Informat Coll, Kunming 650504, Peoples R China

来源：

EIGHTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2016) | 2016年 / 10033卷

关键词：

medical image fusion; multi-objective optimization; particle swarm; pulse coupled neural networks;

D O I：

10.1117/12.2245043

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

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 (Q(AB/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.

引用

页数：6

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