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Zhang, Z. and Blum, R.S. (1999) A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application. Proceedings of the IEEE, 87, 1315-1326. doi:10.1109/5.775414

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• 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.