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Original Article
Design and Implementation of Medical Image Fusion System on FPGA
Mithun S1
Vivek G T2
Jarikala H D3
Arpitha C S4
Lakshmi D L5
Goutham V6
1 2 3 45Department of ECE, BGS Institute of Technology, Bengaluru, Karnataka, India. 6Principal, SJBS Polytechnic, Bengaluru, Karnataka, India.
Published Online: May-June 2025
Pages: 89-93
Cite this article
No DOIReferences
1. S. Li, J. Kwok, and J. Hu, “Medical image fusion using multi-resolution wavelet transform,” Information Fusion, vol. 8, no. 2, pp.
161–175, 2007.
2. D. Zhang and G. Lu, “A review of shape representation and description techniques,” Pattern Recognition, vol. 37, no. 1, pp. 1–19,
2004.
3. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed., Pearson, 2018.
4. J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 4th ed., Pearson, 2006.
5. Xilinx Inc., Vivado Design Suite User Guide, UG910 (v2018.1), 2018. [Online]. Available:
https://www.xilinx.com/support/documentation/sw_manuals/xilinx2018_1/ug910-vivado-getting-started.pdf
6. J. N. Kim, C. H. Lee, and S. J. Ko, “Hardware implementation of medical image fusion using discrete wavelet transform on FPGA,”
IEEE Transactions on Medical Imaging, vol. 37, no. 9, pp. 2035–2044, 2018.
7. M. A. Ismail and M. A. Al-Muhtadi, “FPGA implementation of wavelet-based image fusion,” International Journal of Electronics and
Communication Engineering, vol. 12, no. 1, pp. 25–32, 2019.
8. MathWorks, MATLAB User’s Guide, 2020. [Online]. Available: https://www.mathworks.com/help/matlab/
9. P. Li, J. Wu, and S. Li, “A novel medical image fusion method based on contourlet transform and PCA,” Biomedical Signal Processing
and Control, vol. 13, pp. 85–93, 2014.
10. Y. Zhang and S. Wang, “Real-time implementation of image fusion based on wavelet transform using FPGA,” Journal of Real-Time
Image Processing, vol. 10, no. 2, pp. 287–296, 2015
161–175, 2007.
2. D. Zhang and G. Lu, “A review of shape representation and description techniques,” Pattern Recognition, vol. 37, no. 1, pp. 1–19,
2004.
3. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed., Pearson, 2018.
4. J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 4th ed., Pearson, 2006.
5. Xilinx Inc., Vivado Design Suite User Guide, UG910 (v2018.1), 2018. [Online]. Available:
https://www.xilinx.com/support/documentation/sw_manuals/xilinx2018_1/ug910-vivado-getting-started.pdf
6. J. N. Kim, C. H. Lee, and S. J. Ko, “Hardware implementation of medical image fusion using discrete wavelet transform on FPGA,”
IEEE Transactions on Medical Imaging, vol. 37, no. 9, pp. 2035–2044, 2018.
7. M. A. Ismail and M. A. Al-Muhtadi, “FPGA implementation of wavelet-based image fusion,” International Journal of Electronics and
Communication Engineering, vol. 12, no. 1, pp. 25–32, 2019.
8. MathWorks, MATLAB User’s Guide, 2020. [Online]. Available: https://www.mathworks.com/help/matlab/
9. P. Li, J. Wu, and S. Li, “A novel medical image fusion method based on contourlet transform and PCA,” Biomedical Signal Processing
and Control, vol. 13, pp. 85–93, 2014.
10. Y. Zhang and S. Wang, “Real-time implementation of image fusion based on wavelet transform using FPGA,” Journal of Real-Time
Image Processing, vol. 10, no. 2, pp. 287–296, 2015
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