期刊
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
卷 31, 期 3, 页码 986-1000出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2020.2998696
关键词
Image fusion; Task analysis; Transforms; Information exchange; Image coding; Medical diagnostic imaging; Multimodal medical image fusion; multi-source information exchange encoding; pulse coupled neural network; parameter optimization
资金
- National Natural Science Foundation of China [61833005, 61966037, 61463052]
- China Post-Doctoral Science Foundation [2017M621586]
This paper proposes a novel fusion framework for multimodal medical images based on PCNN and MIEE. The method exchanges and encodes information between different images, producing quantitative fusion contributions. Experimental results show that this approach outperforms other state-of-the-art fusion methods.
Multimodal medical image fusion (MMIF) is to merge multiple images for better imaging quality with preserving different specific features, which could be more informative for efficient clinical diagnosis. In this paper, a novel fusion framework is proposed for multimodal medical images based on multi-source information exchange encoding (MIEE) by using Pulse Coupled Neural Network (PCNN). We construct an MIEE model by using two types of PCNN, such that the information of an image can be exchanged and encoded to another image. Then the fusion contributions for each pixel are estimated qualitatively according to a logical comparison of exchanged information. Further, the exchanged information is nonlinearly transformed using an exponential function with a functional parameter. Finally, quantitative fusion contributions are produced through a reverse-proportional operator to the exchanged information. Also, particle swarm optimization-based derivative-free optimization and a total vibration-based derivative optimization are used to optimize the PCNN and functional transform parameters, respectively. Experiments demonstrate that our method gives the best results than other state-of-the-art fusion approaches.
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