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Application of relative total variation optical decomposition fusion method on medical images

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Abstract

In this paper, we describe a medical image fusion technique based on relative total variation decomposition (RTVD) that can concurrently maintain the texture and contrast information of the input images. The source images are initially separated into structural and texture components based on the relative total variation. The former is mostly composed of the large frame structure and brightness of the source images, while the latter is composed of the texture and noise with low gradient values. Second, distinct fusion weights are created utilizing traits of the structure and texture layers. To maintain the texture information, the weights of texture parts are calculated in accordance with the saliency map, and the weights of structure parts are established in accordance with image energy to maintain the brightness of original images. The fused image could eventually be recreated utilizing sub-images and weights that were previously gathered. We also conduct qualitative and quantitative experiments to verify the effectiveness of the RTVD approach utilizing publically accessible datasets. The findings demonstrate that the RTVD fusion technique performs better than various more sophisticated algorithms in terms of maintaining contrast, preventing edge blurring, and lowering noise. The fusion outcome also more closely matches disease diagnosis.

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References

  1. J.A. Bhutto, L. Tian, Q. Du, Z. Sun, L. Yu, M.F. Tahir, CT and MRI medical image fusion using noise-removal and contrast enhancement scheme with convolutional neural network. Entropy 24(3), 393 (2022)

    Article  ADS  MathSciNet  Google Scholar 

  2. O. Faragallah, H. El-Hoseny, W. El-Shafai, W. El-Rahman, H. El-sayed, S. El-Rabaie, G. Geweid, Optimized multimodal medical image fusion framework using multi-scale geometric and multi-resolution geometric analysis. Multim. Tools Appl. 81(10), 14379–14401 (2022)

    Article  Google Scholar 

  3. R. Ali, F. El-Sayed, W. El-Shafai, T. Elsayed, Efficient fusion of medical images based on CNN. Menoufia J. Electron. Eng. Res. 30(2), 79–83 (2021)

    Article  Google Scholar 

  4. Y. Li, N. Fang, H. Wang, R. Wang, Multi-modal medical image fusion with geometric algebra based sparse representation. Front. Genet. 13(June), 1–9 (2022)

    ADS  Google Scholar 

  5. R. Nandhini Abirami, P. M. Durai Raj Vincent, K. Srinivasan, K. S. Manic, and C. Y. Chang, Multimodal medical image fusion of positron emission tomography and magnetic resonance imaging using generative adversarial networks. Behav. Neurol. 2022, (2022).

  6. Y. Zhou et al., A survey of multi-focus image fusion methods. Appl. Sci. (Switzerland) 12(12), 1–15 (2022)

    Google Scholar 

  7. Y. Gao, S. Ma, J. Liu, Y. Liu, X. Zhang, Fusion of medical images based on salient features extraction by PSO optimized fuzzy logic in NSST domain. Biomed. Signal Process. Control 69(April), 102852 (2021)

    Article  Google Scholar 

  8. S. Shehanaz, E. Daniel, S.R. Guntur, S. Satrasupalli, Optimum weighted multimodal medical image fusion using particle swarm optimization. Optik 231(January), 166413 (2021)

    Article  ADS  Google Scholar 

  9. A. Dogra, B. Goyal, S. Agrawal, From multi-scale decomposition to non-multi-scale decomposition methods: a comprehensive survey of image fusion techniques and its applications. IEEE Access 5, 16040–16067 (2017)

    Article  Google Scholar 

  10. J. Chen, X. Li, K. Wu, Infrared and visible image fusion based on relative total variation decomposition. Infrared Phys. Technol. 123(July 2021), 104112 (2022)

    Article  Google Scholar 

  11. L. Tang, C. Tian, L. Li, B. Hu, W. Yu, K. Xu, Image Communication Perceptual quality assessment for multimodal medical image fusion. Signal Process. 85(December 2019), 115852 (2020)

    Google Scholar 

  12. D.C. Lepcha, A. Dogra, B. Goyal, J.S. Chohan, D. Koundal, Multimodal medical image fusion based on pixel significance using anisotropic diffusion and cross bilateral filter. HCIS. 12, 1–19 (2022)

    Google Scholar 

  13. Y. Zhang, C. Guo, and P. Zhao. Medical image fusion based on low-level features. Comput. Math. Methods Med. 2021, (2021).

  14. A. Dogra and S. Kumar, “Multi-Modality Medical Image Fusion Based on Guided Filter and Image Statistics in Multidirectional Shearlet Transform Domain,” Journal of Ambient Intelligence and Humanized Computing, 0123456789, (2022).

  15. L. Meng, X. Guo, H. Li, MRI/CT fusion based on latent low rank representation and gradient transfer. Biomed. Signal Process. Control 53, 101536 (2019)

    Article  Google Scholar 

  16. Z. Yang, Y. Chen, Z. Le, F. Fan, E. Pan, Multi-source medical image fusion based on wasserstein generative adversarial networks. IEEE Access 7, 175947–175958 (2019)

    Article  Google Scholar 

  17. Q. Wang, X. Yang, An efficient fusion algorithm combining feature extraction and variational optimization for CT and MR images. J. Appl. Clin. Med. Phys. 21(6), 139–150 (2020)

    Article  Google Scholar 

  18. J. Zhao, R. Dhuli, D.P. Liu, G. Bavirisetti, G. **ao, Multi-scale guided image and video fusion: a fast and efficient approach. Circ. Syst. Signal Process. 38(12), 5576–5605 (2019)

    Article  Google Scholar 

  19. Y. Liu, X. Yang, R. Zhang, M.K. Albertini, T. Celik, G. Jeon, Entropy-based image fusion with joint sparse representation and rolling guidance filter. Entropy 22(1), 118 (2020)

    Article  ADS  Google Scholar 

  20. L. Barba-J, L. Vargas-Quintero, and J. A. Calderón-Agudelo, “Bone SPECT/CT image fusion based on the discrete hermite transform and sparse representation,” Biomedical Signal Processing and Control, 71(August 2021), (2022).

  21. M. Kaur, D. Singh, Fusion of medical images using deep belief networks. Clust. Comput. 23(2), 1439–1453 (2020)

    Article  Google Scholar 

  22. D.S. Shibu, S.S. Priyadharsini, Multi scale decomposition based medical image fusion using convolutional neural network and sparse representation. Biomed. Signal Process. Control 69(April), 102789 (2021)

    Article  Google Scholar 

  23. P.H. Dinh, Multi-modal medical image fusion based on equilibrium optimizer algorithm and local energy functions. Appl. Intell. 51(11), 8416–8431 (2021)

    Article  Google Scholar 

  24. W. Tan, P. Tiwari, H. M. Pandey, C. Moreira, and A. K. Jaiswal, “Multimodal Medical Image Fusion Algorithm in The Era Of Big Data,” Neural Computing and Applications, 2, (2020).

  25. Z. Ding, D. Zhou, H. Li, R. Hou, Y. Liu, Siamese networks and multi-scale local extrema scheme for multimodal brain medical image fusion. Biomed. Signal Process. Control 68(August 2020), 102697 (2021)

    Article  Google Scholar 

  26. S. Liu, M. Wang, L. Yin, X. Sun, Y.D. Zhang, J. Zhao, Two-scale multimodal medical image fusion based on structure preservation. Front. Comput. Neurosci. 15(January), 1–14 (2022). https://doi.org/10.3389/fncom.2021.803724

    Article  Google Scholar 

  27. X. Li, F. Zhou, H. Tan, W. Zhang, C. Zhao, Multimodal medical image fusion based on joint bilateral filter and local gradient energy. Inf. Sci. 569, 302–325 (2021)

    Article  MathSciNet  Google Scholar 

  28. C. Ghandour, W. El-Shafai, and S. El-Rabaie, “Comparative Study between Different Image Fusion Techniques Applied on Biomedical Images,” In 2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) (pp. 164–169). IEEE, (2021, December).

  29. Y. Liu, L. Wang, J. Cheng, C. Li, X. Chen, Multi-focus image fusion: a survey of the State of the Art. Inform. Fusion 64(June), 71–91 (2020)

    Article  Google Scholar 

  30. C. Ghandour, W. El-Shafai, and S. El-Rabaie, “Medical Image Fusion Based on Weighted Least Square Optimization and Deep Learning Algorithm,” In 2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) (pp. 159–163). IEEE (2021).

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Authors and Affiliations

  1. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    C. Ghandour, Walid El-Shafai & S. El-Rabaie

  2. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, International Academy of Engineering and Media Science (IAEMS), Giza, Egypt

    C. Ghandour

  3. Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia

    Walid El-Shafai

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  1. C. Ghandour
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Ghandour, C., El-Shafai, W. & El-Rabaie, S. Application of relative total variation optical decomposition fusion method on medical images. J Opt 52, 845–859 (2023). https://doi.org/10.1007/s12596-022-01032-6

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  • DOI: https://doi.org/10.1007/s12596-022-01032-6

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