The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
A Phase Congruency and Local Laplacian Energy Based Multi-Modality Medical Image Fusion Method in NSCT Domain
Open accessAbstract
Multi-modality image fusion provides more comprehensive and sophisticated information in modern medical diagnosis, remote sensing, video surveillance, and so on. This paper presents a novel multi-modality medical image fusion method based on phase congruency and local Laplacian energy. In the proposed method, the non-subsampled contourlet transform is performed on medical image pairs to decompose the source images into high-pass and low-pass subbands. The high-pass subbands are integrated by a phase congruency-based fusion rule that can enhance the detailed features of the fused image for medical diagnosis. A local Laplacian energy-based fusion rule is proposed for low-pass subbands. The local Laplacian energy consists of weighted local energy and the weighted sum of Laplacian coefficients that describe the structured information and the detailed features of source image pairs, respectively. Thus, the proposed fusion rule can simultaneously integrate two key components for the fusion of low-pass subbands. The fused high-pass and low-pass subbands are inversely transformed to obtain the fused image. In the comparative experiments, three categories of multi-modality medical image pairs are used to verify the effectiveness of the proposed method. The experiment results show that the proposed method achieves competitive performance in both the image quantity and computational costs.
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Cites
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Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain
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Cited by
SDNet: A Versatile Squeeze-and-Decomposition Network for Real-Time Image Fusion
Zhang, H., & Ma, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01501-8 · Cited by 747
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
Li, Y., El Habib Daho, M., Conze, P.-H., Zeghlache, R., Le Boité, H., Tadayoni, R., Cochener, B., Lamard, M., & Quellec, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108635 · Cited by 274
A review of multimodal medical image fusion techniques
Huang, B., Yang, F., Yin, M., Mo, X., & Zhong, C. (2020) · Computational and Mathematical Methods in Medicine · doi:10.1155/2020/8279342 · Cited by 220
Metadata
- Authors
- Zhu, Z., Zheng, M., Qi, G., Wang, D., & Xiang, Y.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 20811-20824
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2912551047
- Cited by
- 328
- References
- 37
- FWCI
- 31.97
- Type
- journal article
- Language
- en
- Open access
- gold
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