Medical images fusion using two-stage combined model DWT and DCT

The purpose of image enhancement is to improve the interpretation or perception of information in the image for viewers and the input of automated processing systems. Combining a multicenter image is a way of combining several images on a screen, focusing on different objects so that all objects appear in focus in the final image. Wavelet transform and cosine transform are used in many image processing applications, including image fusion. This paper's technique will combine DWT and DCT in two steps for medical MRI and PET images, eventually extracting the combined image. Input images are first divided into 8-pixel blocks in which DCT coefficients are extracted. After extracting the DCT coefficients, the first step of the combination takes place. The re-images will then be combined with the DWT conversion. According to the presented data, the proposed method achieved up to 5% better combination and, as a result, better image quality than the single-stage DWT and DCT methods.

___

  • 1. Seo, Y., Y. Choi, and J. Choi, River stage modeling by combining maximal overlap discrete wavelet transform, support vector machines and genetic algorithm. Water, 2017. 9(7): p. 525.
  • 2. Amiri, E., et al., Detection Of Topographic Images Of Keratoconus Disease Using Machine Vision. International Journal of Engineering Science and Application, 2020. 4(4): p. 145-150.
  • 3. Xu, X., et al., Multimodal medical image fusion using PCNN optimized by the QPSO algorithm. Applied Soft Computing, 2016. 46: p. 588-595.
  • 4. Daniel, E., et al., Optimum spectrum mask based medical image fusion using Gray Wolf Optimization. Biomedical Signal Processing and Control, 2017. 34: p. 36-43.
  • 5. Nair, R.R. and T. Singh, Multi-sensor medical image fusion using pyramid-based DWT: a multi-resolution approach. IET Image Processing, 2019. 13(9): p. 1447-1459.
  • 6. Meher, B., et al., A survey on region based image fusion methods. Information Fusion, 2019. 48: p. 119-132.
  • 7. Zhao, W., et al. Local Binary Pattern Metric-Based Multi-focus Image Fusion. in International Symposium on Artificial Intelligence and Robotics. 2018. Springer.
  • 8. Xiao, J., et al., Multi-focus image fusion based on depth extraction with inhomogeneous diffusion equation. Signal Processing, 2016. 125: p. 171-186.
  • 9. El-Hoseny, H.M., et al., An efficient DT-CWT medical image fusion system based on modified central force optimization and histogram matching. Infrared Physics & Technology, 2018. 94: p. 223-231.
  • 10. Ma, J. and D. Zhang, An image fusion method based on content cognition. Procedia computer science, 2018. 131: p. 177-181.
  • 11. Manchanda, M. and R. Sharma, An improved multimodal medical image fusion algorithm based on fuzzy transform. Journal of Visual Communication and Image Representation, 2018. 51: p. 76-94.
  • 12. Cabazos-Marín, A.R. and J. Álvarez-Borrego, Automatic focus and fusion image algorithm using nonlinear correlation: Image quality evaluation. Optik, 2018. 164: p. 224-242.
  • 13. Gharbia, R., et al., Multi-spectral and panchromatic image fusion approach using stationary wavelet transform and swarm flower pollination optimization for remote sensing applications. Future Generation Computer Systems, 2018. 88: p. 501-511.
  • 14. Farid, M.S., A. Mahmood, and S.A. Al-Maadeed, Multi-focus image fusion using content adaptive blurring. Information fusion, 2019. 45: p. 96-112.
  • 15. Xu, X., Y. Wang, and S. Chen, Medical image fusion using discrete fractional wavelet transform. Biomedical signal processing and control, 2016. 27: p. 103-111.
  • 16. Zhang, P., et al., Infrared and visible image fusion using co-occurrence filter. Infrared Physics & Technology, 2018. 93: p. 223-231.
  • 17. Paramanandham, N. and K. Rajendiran, Infrared and visible image fusion using discrete cosine transform and swarm intelligence for surveillance applications. Infrared Physics & Technology, 2018. 88: p. 13-22.
  • 18. Yin, W., et al., Local binary pattern metric-based multi-focus image fusion. Optics & Laser Technology, 2019. 110: p. 62-68.
  • 19. Tian, J., G. Liu, and J. Liu, Multi-focus image fusion based on edges and focused region extraction. Optik, 2018. 171: p. 611-624.
  • 20. Joshi, K., et al. Multi-Focus Image Fusion Using Discrete Wavelet Transform Method. in International Conference on Advances in Engineering Science Management & Technology (ICAESMT)-2019, Uttaranchal University, Dehradun, India. 2019.
  • 21. Ozsoydan, F.B., Effects of dominant wolves in grey wolf optimization algorithm. Applied Soft Computing, 2019. 83: p. 105658.
  • 22. Nobariyan, B., et al., A Novel Architecture of Medical Image Fusion Based on YCbCr-DWT Transform. INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, 2018. 15(5): p. 850-856.
  • 23. Daneshvar, S. and H. Ghassemian, MRI and PET image fusion by combining IHS and retina-inspired models. Information fusion, 2010. 11(2): p. 114-123.
  • 24. Du, J., et al., An overview of multi-modal medical image fusion. Neurocomputing, 2016. 215: p. 3-20.
  • 25. Harward University website [cited 2020 29 June]; Available from: http://www.med.harvard.edu/AANLIB/home.html.
  • 26. Javed, U., et al., MRI and PET image fusion using fuzzy logic and image local features. The Scientific World Journal, 2014. 2014.
  • 27. Diwakar, M., et al., A comparative review: Medical image fusion using SWT and DWT. Materials Today: Proceedings, 2020.