The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain
Abstract
As an effective way to integrate the information contained in multiple medical images with different modalities, medical image fusion has emerged as a powerful technique in various clinical applications such as disease diagnosis and treatment planning. In this paper, a new multimodal medical image fusion method in nonsubsampled shearlet transform (NSST) domain is proposed. In the proposed method, the NSST decomposition is first performed on the source images to obtain their multiscale and multidirection representations. The high-frequency bands are fused by a parameter-adaptive pulse-coupled neural network (PA-PCNN) model, in which all the PCNN parameters can be adaptively estimated by the input band. The low-frequency bands are merged by a novel strategy that simultaneously addresses two crucial issues in medical image fusion, namely, energy preservation and detail extraction. Finally, the fused image is reconstructed by performing inverse NSST on the fused high-frequency and low-frequency bands. The effectiveness of the proposed method is verified by four different categories of medical image fusion problems [computed tomography (CT) and magnetic resonance (MR), MR-T1 and MR-T2, MR and positron emission tomography, and MR and single-photon emission CT] with more than 80 pairs of source images in total. Experimental results demonstrate that the proposed method can obtain more competitive performance in comparison to nine representative medical image fusion methods, leading to state-of-the-art results on both visual quality and objective assessment.
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Azam, M. A., Khan, K. B., Salahuddin, S., Rehman, E., Khan, S. A., Khan, M. A., Kadry, S., & Gandomi, A. H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105253 · Cited by 449
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Self-citationDM-Fusion: Deep Model-Driven Network for Heterogeneous Image Fusion
Xu, G., He, C., Wang, H., Zhu, H., & Ding, W. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3238511 · Cited by 57
Degradation-Resistant Unfolding Network for Heterogeneous Image Fusion
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Metadata
- Authors
- Yin, M., Liu, X., Liu, Y., & Chen, X.
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2019-01
- Volume
- 68
- Issue
- 1
- Pages
- 49-64
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W2808591023
- Cited by
- 594
- References
- 44
- FWCI
- 41.45
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yin, M., Liu, X., Liu, Y., & Chen, X. (2019). Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/tim.2018.2838778
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