International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Medical Image Fusion on Latent Low Rank Representation and Hesitant Fuzzy Granular Energy |
|---|---|
| ABSTRACT | Multi-modal medical fused images encapsulate rich pathological tissue information, offering critical support for clinical decision-making and assisted diagnosis. However, prevailing medical image fusion methodologies exhibit deficiencies in concurrently enhancing visual fidelity and preserving intrinsic energy information of tissues. For this purpose, we construct a new fusion framework of MRI and PET joint latent low rank representation with truncated Huber filtering. Under the proposed framework, feature information, low frequency parts and high frequency parts can be obtained. To effectively detect energy information of tissues in medical images, we propose a hesitant fuzzy granular energy which imitates the cognitive ability of human beings with multiple levels and perspectives. And hesitant fuzzy granular energy with fast guide filter is constructed to fuse low frequency parts. Also, we propose the directional pseudo spatial frequency to detect fine size information on oblique directions of tissues. And directional pseudo spatial frequency with dual channel PCNN is employed for fusing high frequency parts and feature information. Experimental results display that the proposed fusion method has a superior performance in visual perception, and also has advantages in a variety of evaluation metrics, by comparing with 10 state-of-the-art methods. |
| AUTHOR | CH.L N SWAMY, M.N S VAMSI, M.JYOTHSNA U V N S, S.CHETAN PAVAN, D.SRIDHAR U.G. Student, Department of ECE, SVIET Engineering College, Nandamuru, Pedana, Andhra Pradesh, India Associate Professor, Department of ECE, SVIET Engineering College, Nandamuru, Pedana, Andhra Pradesh, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403116 |
| pdf/116_Medical Image Fusion on Latent Low Rank Representation and Hesitant Fuzzy Granular Energy.pdf | |
| KEYWORDS | |
| References | 1. Azam, M. A., Khan, K. B., et al. (2022). A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics. Computer Biology and Medicine, 144, 105253. 2. Chen, S., He, K., Xu, D., et al. (2025). A dual-domain framework for multimodal medical image registration: Optimizing phase consistency with LPC-GIMI. Biomedical Signal Processing and Control, 99, 106809. 3. Dinh, P. H. (2025). Medical image fusion based on bilateral texture filter and transfer learning with the ResNet-101 network. Biomedical Signal Processing and Control, 100, 106976. 4. Du, J., Li, W., & Xiao, B. (2017). Anatomical-functional image fusion by local Laplacian filtering. IEEE Transactions on Image Processing, 26(12), 5855–5866. 5. Duan, J., Xiong, J., Li, Y., et al. (2024). Deep learning based multimodal biomedical data fusion: An overview and comparative review. Information Fusion, 112, 102536. 6. Fu, Z., Zhao, Y., Chang, D., et al. (2023). Latent low-rank representation with weighted distance penalty for clustering. IEEE Transactions on Cybernetics, 53(11), 6870–6882. 7. Goyal, B., Dogra, A., Lepcha, D. C., et al. (2022). Multi-modality image fusion for medical assistive technology management based on hybrid domain filtering. Expert Systems with Applications, 209, 118283. 8. Hermessi, H., Mourali, O., & Zagrouba, E. (2021). Multimodal medical image fusion review: Theoretical background and recent advances. Signal Processing, 183, 108036. 9. Hou, R., Zhou, D., Nie, R., et al. (2019). Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model. Medical & Biological Engineering & Computing, 57, 887–900. 10. Huang, W., Zhang, H., Quan, X., et al. (2022). A two-level dynamic adaptive network for medical image fusion. IEEE Transactions on Instrumentation and Measurement, 71, 1–9. 11. Jie, Y., Li, X., Zhou, F., et al. (2023). Medical image fusion based on edge-preserving techniques. Expert Systems with Applications, 227, 120301. 12. Li, X., Zhou, F., & Tan, H. (2021). Joint image fusion and denoising via three-layer decomposition and sparse representation. Knowledge-Based Systems, 224, 107087. |
