Medical image fusion based on pulse coupled neural network combining with compressive sensing

被引：0

作者：

Wang, Aili ^{[1
]}

Zhao, Jiaying ^{[1
]}

Dai, Shiyu ^{[1
]}

Iwahori, Yuji ^{[2
]}

Zhao, Yangyang ^{[1
]}

机构：

[1] Higher Education Key Lab for Measuring and Control Technology and Instrumentations of Heilongjiang, Harbin, China

[2] Dept of Computer Science, Chubu University, Japan

来源：

International Journal of Signal Processing, Image Processing and Pattern Recognition | 2015年 / 8卷 / 05期

关键词：

Medical imaging - Image compression - Efficiency - Compressed sensing - Neural networks;

D O I：

10.14257/ijsip.2015.8.5.23

中图分类号：

学科分类号：

摘要：

Image fusion is an important branch of information fusion, widely used in various fields, especially in medical field. So increasing the quality and efficiency of medical image fusion has great significance. Combining the advantages of pulse coupled neural networks with Compressive Sensing; this paper puts forward a novel image fusion method in NSCT transform domain. First, NSCT transform is applied to the source images, and the coefficients in low frequency coefficient are fused by mean rules. For high frequency coefficient, CS is applied and PCNN. Finally, inverse NSCT is applied to get the reconstructed image. The experimental results show that the fusion algorithm proposed in this paper in the performance and integration efficiency has better fusion results. © 2015 SERSC.

引用

页码：223 / 230