GIẢI THUẬT TỔNG HỢP HÌNH ẢNH Y HỌC DỰA TRÊN COA VÀ CẤU TRÚC TENSOR
Nội dung chính của bài viết
Tóm tắt
Tổng hợp hình ảnh y học đóng vai trò quan trọng trong chẩn đoán bệnh dựa trên hình ảnh. Tuy nhiên, các giải thuật hiện tại thường gặp nhược điểm như mất thông tin và suy giảm cường độ sáng. Bài báo này đề xuất một giải thuật mới nhằm khắc phục các vấn đề trên. Ảnh đầu vào được phân rã thành các thành phần tần số thấp và tần số cao bằng các bộ lọc hướng dẫn vòng quay và Gaussian. Thành phần tần số cao được tổng hợp bằng cấu trúc Tensor kết hợp hàm năng lượng cục bộ, trong khi tần số thấp được tổng hợp bằng luật tổng hợp thích nghi dựa trên COA. Thử nghiệm trên 36 cặp ảnh MRI/PET cho thấy phương pháp này vượt trội so với năm giải thuật công bố từ 2020-2024.
Từ khóa
Cấu trúc Tensor, COA, tổng hợp hình ảnh y học
Chi tiết bài viết
Tài liệu tham khảo
[1] B. Huang, F. Yang, M. Yin, X. Mo, C. Zhong (2020), A Review of Multimodal Medical Image Fusion Techniques, Computational and Mathematical Methods in Medicine, vol. Hindawi Limited, doi: 10.1155/2020/8279342.
[2] M. A. Azam et al. (2022), A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics, Comput Biol Med, vol. 144, doi: 10.1016/j.compbiomed.2022.105253.
[3] A. P. James, B. V. Dasarathy (2014), Medical image fusion: A survey of the state of the art, Information Fusion, 19(1), 4-19, doi: 10.1016/j.inffus.2013.12.002.
[4] S. Bhat, D. Koundal (2021), Multi-focus image fusion techniques: a survey, Artif Intell Rev, 54(8), 5735-5787, doi: 10.1007/s10462-021-09961-7.
[5] P. H. Dinh (2021), A novel approach based on Grasshopper optimization algorithm for medical image fusion, Expert Syst Appl, vol. 171, doi: 10.1016/j.eswa.2021.114576.
[6] Oanh Cuong Do, Chi Mai Luong, Phu-Hung Dinh, Giang Son Tran (2024), An efficient approach to medical image fusion based on optimization and transfer learning with VGG19, Biomedical Signal Processing and Control, vol.87, 105370, https://doi.org/10.1016/j.bspc.2023.105370.
[7] V. S. Parvathy, S. Pothiraj (2020), Multi-modality medical image fusion using hybridization of binary crow search optimization, Health Care Management Science, vol.23, 661-669 doi: 10.1007/s10729-019-09492-2/Published.
[8] T.-H.-H. Le, P.-H. Dinh, V.-H. Vu, N. L. Giang (2024), A new approach to medical image fusion based on the improved extended difference-of-gaussians combined with the coati optimization algorithm, Biomedical Signal Processing and Control, 93, 106175, doi:10.1016/j.bspc.2024.106175.
[9] J. Ma, Z. Zhou, B. Wang, H. Zong (2017), Infrared and visible image fusion based on visual saliency map and weighted least square optimization, Infrared Phys Technol, vol.82, 8-17, doi: 10.1016/j.infrared.2017.02.005.
[10] M. Dehghani, Z. Montazeri, E. Trojovská, P. Trojovský (2023), Coati optimization algorithm: A new bioinspired metaheuristic algorithm for solving optimization problems, Knowledge-Based Systems, doi:10.1016/j.knosys.2022.110011.
[11] W. Zhang, J. Fehrenbach, A. Desmaison, V. Lobjois, B. Ducommun, P. Weiss (2016), Structure tensor based analysis of cells and nuclei organization in tissues, IEEE Trans Med Imaging, vol. 35, no. 1, pp. 294–306, doi: 10.1109/TMI.2015.2470093.
[12] Z. Li, D. Mahapatra, J. A. W. Tielbeek, J. Stoker, L. J. Van Vliet, F. M. Vos (2016), Image Registration Based on Autocorrelation of Local Structure, IEEE Trans Med Imaging, 35(1), 63-75, doi: 10.1109/TMI.2015.2455416.
[13] X. Li, F. Zhou, H. Tan, W. Zhang, C. Zhao (2021), Multimodal medical image fusion based on joint bilateral filter and local gradient energy, Inf Sci (N Y), vol.569, 302-325, doi: 10.1016/j.ins.2021.04.052.
[14] C. Xydeas, V. Petrovic (2000), Objective image fusion performance measure, Electronics Letters 36, 308, doi:10.1049/el:20000267.
[15] L. Zhang, L. Zhang, X. Mou, D. Zhang (2011), Fsim: A feature similarity index for image quality assessment, IEEE Transactions on Image Processing 20, 2378-2386. doi:10.1109/TIP.2011.2109730.
[16] J. Zhao, R. Laganiere, Z. Liu (2007), Performance assessment of combinative pixel-level image fusion based on an absolute feature measurement, International Journal of Innovative Computing, Information and Control 3 (6), 1433-1447.
[17] B. Li, H. Peng, X. Luo, J. Wang, X. Song, M. J. Pérez-Jiménez, A. Riscos-Núñez (2020), Medical image fusion method based on coupled neural p systems in nonsubsampled shearlet transform domain, International Journal of Neural Systems 31(01), 2050050.doi:10.1142/s0129065720500501.
[18] W. Tan, W. Thitøn, P. Xiang, H. Zhou (2021), Multi-modal brain image fusion based on multi-level edge-preserving filtering, Biomedical Signal Processing and Control 64, 102280, doi:10.1016/j.bspc.2020.102280.
[19] B. Li, H. Peng, J. Wang (2021), A novel fusion method based on dynamic threshold neural p systems and nonsubsampled contourlet transform for multi-modality medical images, Signal Processing 178, 107793, doi:10.1016/j.sigpro.2020.107793.
[20] Y. Jie, X. Li, H. Tan, F. Zhou, G. Wang (2024), Multi-modal medical image fusion via multi-dictionary and truncated huber filtering, Biomedical Signal Processing and Control 88, 105671, doi:10.1016/j.bspc.2023.105671.
[21] https://www.med.harvard.edu/aanlib/cases/caseNA/pb9.htm.
[2] M. A. Azam et al. (2022), A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics, Comput Biol Med, vol. 144, doi: 10.1016/j.compbiomed.2022.105253.
[3] A. P. James, B. V. Dasarathy (2014), Medical image fusion: A survey of the state of the art, Information Fusion, 19(1), 4-19, doi: 10.1016/j.inffus.2013.12.002.
[4] S. Bhat, D. Koundal (2021), Multi-focus image fusion techniques: a survey, Artif Intell Rev, 54(8), 5735-5787, doi: 10.1007/s10462-021-09961-7.
[5] P. H. Dinh (2021), A novel approach based on Grasshopper optimization algorithm for medical image fusion, Expert Syst Appl, vol. 171, doi: 10.1016/j.eswa.2021.114576.
[6] Oanh Cuong Do, Chi Mai Luong, Phu-Hung Dinh, Giang Son Tran (2024), An efficient approach to medical image fusion based on optimization and transfer learning with VGG19, Biomedical Signal Processing and Control, vol.87, 105370, https://doi.org/10.1016/j.bspc.2023.105370.
[7] V. S. Parvathy, S. Pothiraj (2020), Multi-modality medical image fusion using hybridization of binary crow search optimization, Health Care Management Science, vol.23, 661-669 doi: 10.1007/s10729-019-09492-2/Published.
[8] T.-H.-H. Le, P.-H. Dinh, V.-H. Vu, N. L. Giang (2024), A new approach to medical image fusion based on the improved extended difference-of-gaussians combined with the coati optimization algorithm, Biomedical Signal Processing and Control, 93, 106175, doi:10.1016/j.bspc.2024.106175.
[9] J. Ma, Z. Zhou, B. Wang, H. Zong (2017), Infrared and visible image fusion based on visual saliency map and weighted least square optimization, Infrared Phys Technol, vol.82, 8-17, doi: 10.1016/j.infrared.2017.02.005.
[10] M. Dehghani, Z. Montazeri, E. Trojovská, P. Trojovský (2023), Coati optimization algorithm: A new bioinspired metaheuristic algorithm for solving optimization problems, Knowledge-Based Systems, doi:10.1016/j.knosys.2022.110011.
[11] W. Zhang, J. Fehrenbach, A. Desmaison, V. Lobjois, B. Ducommun, P. Weiss (2016), Structure tensor based analysis of cells and nuclei organization in tissues, IEEE Trans Med Imaging, vol. 35, no. 1, pp. 294–306, doi: 10.1109/TMI.2015.2470093.
[12] Z. Li, D. Mahapatra, J. A. W. Tielbeek, J. Stoker, L. J. Van Vliet, F. M. Vos (2016), Image Registration Based on Autocorrelation of Local Structure, IEEE Trans Med Imaging, 35(1), 63-75, doi: 10.1109/TMI.2015.2455416.
[13] X. Li, F. Zhou, H. Tan, W. Zhang, C. Zhao (2021), Multimodal medical image fusion based on joint bilateral filter and local gradient energy, Inf Sci (N Y), vol.569, 302-325, doi: 10.1016/j.ins.2021.04.052.
[14] C. Xydeas, V. Petrovic (2000), Objective image fusion performance measure, Electronics Letters 36, 308, doi:10.1049/el:20000267.
[15] L. Zhang, L. Zhang, X. Mou, D. Zhang (2011), Fsim: A feature similarity index for image quality assessment, IEEE Transactions on Image Processing 20, 2378-2386. doi:10.1109/TIP.2011.2109730.
[16] J. Zhao, R. Laganiere, Z. Liu (2007), Performance assessment of combinative pixel-level image fusion based on an absolute feature measurement, International Journal of Innovative Computing, Information and Control 3 (6), 1433-1447.
[17] B. Li, H. Peng, X. Luo, J. Wang, X. Song, M. J. Pérez-Jiménez, A. Riscos-Núñez (2020), Medical image fusion method based on coupled neural p systems in nonsubsampled shearlet transform domain, International Journal of Neural Systems 31(01), 2050050.doi:10.1142/s0129065720500501.
[18] W. Tan, W. Thitøn, P. Xiang, H. Zhou (2021), Multi-modal brain image fusion based on multi-level edge-preserving filtering, Biomedical Signal Processing and Control 64, 102280, doi:10.1016/j.bspc.2020.102280.
[19] B. Li, H. Peng, J. Wang (2021), A novel fusion method based on dynamic threshold neural p systems and nonsubsampled contourlet transform for multi-modality medical images, Signal Processing 178, 107793, doi:10.1016/j.sigpro.2020.107793.
[20] Y. Jie, X. Li, H. Tan, F. Zhou, G. Wang (2024), Multi-modal medical image fusion via multi-dictionary and truncated huber filtering, Biomedical Signal Processing and Control 88, 105671, doi:10.1016/j.bspc.2023.105671.
[21] https://www.med.harvard.edu/aanlib/cases/caseNA/pb9.htm.