Abstract
Medical image registration plays a dominant role in medical image analysis and clinical research. In this paper, we present a new coarse-to-fine method based on pulse-coupled neural networks (PCNNs) and mutual information (MI). In the coarse-registration process, we use the PCNN-clusters’ invariant characteristics of translation, rotation and distortion to get the coarse parameters. And the parameters of the PCNN model are optimized by ant colony optimization algorithm. In the fine-registration process, the coarse parameters provide a near-optimal initial solution. Based on this, the fine-tuning process is implemented by mutual information using the particle swarm optimization algorithm to search the optimal parameters. For the purpose of proving the proposed method can deal with medical image registration automatically, the experiments are carried out on MR and CT images. The comparative experiments on MI-based and SIFT-based methods for medical image registration show that the proposed method achieves higher performance in accuracy.
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Acknowledgments
This work is supported by the Fundamental Research Funds for the Central Universities (No. 2013QNA24), the Basic Research Program (Natural Science Foundation) of Jiangsu Province of China (No. BK20130209) and the National Natural Science Foundation of China (No. 61379101).
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Wang, G., Xu, X., Jiang, X. et al. Medical image registration based on self-adapting pulse-coupled neural networks and mutual information. Neural Comput & Applic 27, 1917–1926 (2016). https://doi.org/10.1007/s00521-015-1985-x
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DOI: https://doi.org/10.1007/s00521-015-1985-x