TITLE:
Medical image fusion based on pulse coupled neural networks and multi-feature fuzzy clustering
AUTHORS:
Xiaoqing Luo, Xiaojun Wu
KEYWORDS:
PCNN; Multi-Feature; Medical Image; Image Fusion; Local Entropy
JOURNAL NAME:
Journal of Biomedical Science and Engineering,
Vol.5 No.12A,
December
31,
2012
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.