Medical image fusion based on pulse coupled neural networks and multi-feature fuzzy clustering

Xiaoqing Luo, Xiaojun Wu

School of IoT Engineering, Jiangnan University, Wuxi, China.

DOI: 10.4236/jbise.2012.512A111                

Abstract

Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, noninvasive diagnosis, and treatment planning. In order to retain useful information and get more reliable results, a novel medical image fusion algorithm based on pulse coupled neural networks (PCNN) and multi-feature fuzzy clustering is proposed, which makes use of the multi-feature of image and combines the advantages of the local entropy and variance of local entropy based PCNN. The results of experiments indicate that the proposed image fusion method can better preserve the image details and robustness and significantly improve the image visual effect than the other fusion methods with less information distortion.

Keywords

PCNN; Multi-Feature; Medical Image; Image Fusion; Local Entropy

Conflicts of Interest

The authors declare no conflicts of interest.

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