A Novel Medical Image Fusion Method Using Multi-Channel Pulse Coupled Neural Networks

被引:4
|
作者
Li, Yi [1 ]
Zhao, Junli [1 ]
机构
[1] Qingdao Univ, Sch Data Sci & Software Engn, Qingdao 266071, Peoples R China
基金
中国国家自然科学基金;
关键词
Image fusion; Computational modeling; Hidden Markov models; Neural networks; Transforms; Biomedical imaging; Ignition; medical image; neural networks; pulse coupled neural networks; DUAL-CHANNEL PCNN; TRANSFORM;
D O I
10.1109/ACCESS.2020.3019426
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A novel method for multi-channel pulse coupled neural networks (PCNN) is proposed in order to overcome the difficulty in lack of M-PCNN and question of many images fusion and instability of double-channel PCNN due to the different fusion sequence. In this study a novel multi-channel pulse coupled neural networks is analyzed in detail from odd numbers channel PCNN and even numbers channel PCNN. Through a combination of odd numbers channel PCNN and even numbers channel PCNN this study can realize image fusion by M-PCNN. A novel calculation method about linking-weight based on gray energy is proposed in order to overcome the lack of linking-weight in M-PCNN. To effectively avoid the Laplasse operator and calculation repeatedly, this study can realize not only many images fusion, but also better time efficiency and less information loss. Experimental results show that the presented method outperforms existing methods, in both fusion effect and different performance evaluation criteria.
引用
收藏
页码:157572 / 157586
页数:15
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