Medical Image Fusion Multi Model Based on Quaternion Wavelet Transform

Authors

  • V. Supraja  Assistant Professor, Department of ECE, Ravindra college of Engineering for Women, Kurnool, Andhra Pradesh, India
  • K. Swetha  Department of ECE, Ravindra College of Engineering for Women, Kurnool, Andhra Pradesh, India
  • E. Haritha  Department of ECE, Ravindra College of Engineering for Women, Kurnool, Andhra Pradesh, India
  • K. Sumalatha  Department of ECE, Ravindra College of Engineering for Women, Kurnool, Andhra Pradesh, India
  • G. Chandrika  Department of ECE, Ravindra College of Engineering for Women, Kurnool, Andhra Pradesh, India

DOI:

https://doi.org//10.32628/IJSRSET1229264

Keywords:

Medical Image Fusion, Quaternion Wavelet Transform, Context, Activity Measure

Abstract

Medical image fusion can combine multi-modal images into an integrated higher-quality image, which can provide more comprehensive and accurate pathological information than individual image does. Traditional transform domain-based image fusion methods usually ignore the dependencies between coefficients and may lead to the inaccurate representation of source image. To improve the quality of fused image, a medical image fusion method based on the dependencies of quaternion wavelet transform coefficients is proposed. First, the source images are decomposed into low-frequency component and high-frequency component by quaternion wavelet transform. Then, a clarity evaluation index based on quaternion wavelet transform amplitude and phase is constructed and a contextual activity measure is designed. These measures are utilized to fuse the high-frequency coefficients and the choose-max fusion rule is applied to the lowfrequency components. Finally, the fused image can be obtained by inverse quaternion wavelet transform. The experimental results on some brain multi-modal medical images demonstrate that the proposed method has achieved advanced fusion result.

References

  1. Selvakumari G and Aravindh R. Directive contrast based multimodal medical image fusion with nonsubsampled contour let transform. Int J Eng Res Technol 2014; 3: 38–42.
  2. Morris C, Rajesh R. S.. Survey of spatial domain image fusion techniques. J Adv Res Com Sc Eng and Info Tech 2014; 2: 249–254.
  3. Yang Y. Multimodal Medical image fusion through a new DWT based technique. In: International Conferenceon Bioinformatics and Biomedical Engineering, 2010, pp.1–4. Chengdu, CN: IEEE.
  4. Yan Y, Li L and Yang Y. Image fusion based on principal component analysis in dual-tree complex wavelet transform domain. In: International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP), 2012, pp.70–73. Chengdu, CN: IEEE.
  5. He Z, Zhang D, Zhang L, et al. Fusion of polarimetric image using contourlet transform. In: International Conference on Consumer Electronics, Communications and Networks (CECNet), 2011, pp.362–365. Xianning, CN: IEEE.
  6. Zhang Q and Guo B. Research on image fusion based on the nonsubsampled contourlet transform. In: International Conference on Control and Automation, 2007, pp.3239–3243. Guangzhou, CN: IEEE.
  7. Bayro-Corrochano E. The theory and use of the quaternion wavelet transform. J Math Imaging Vis 2006; 24: 19–35.
  8. Gai S, Yang G and Zhang S. Multiscale texture classification using reduced quaternion wavelet transform. AEU Int J Electron Commun 2013; 67: 233–241.
  9. Liu Y, Jin J and Wang Q. Region level based multi-focus image fusion using quaternion wavelet and normalized cut. Signal Process 2014; 97: 9–30.
  10. Zheng X, Luo X, Zhang Z, et al. Multi-focus image fusion using quaternion wavelet transform. In: International Conference on Pattern Recognition, 2016, pp.883–888. Cancun, MX: IEEE.
  11. Chai P, Luo X and Zhang Z. Image fusion using quaternion wavelet transform and multiple features. IEEE Access 2017; 5: 6724–6734.
  12. Zhang H, Luo X, Wu X, et al. Statistical modeling of multi-modal medical image fusion method using C-CHMM and M-PCNN. In: International Conference on Pattern Recognition, 2014, pp.1067–1072. Stockholm, SE: IEEE.
  13. Mumtaz A, Choi TS, Majid A, et al. Image fusion algorithm based on dual tree complex wavelet transform and support vector machine. In: International Bhurban Conference on Applied Sciences & Technology, 2010, pp. 197–202. Islamabad, PK: IEEE.
  14. Patil U and Mudengudi U. Image fusion using hierarchical PCA. In: International Conference on Image Information Processing, 2011, pp.1–6. Shimla, IN: IEEE.
  15. Li J, Yu J and Sun S. Study of image fusion based on grad pyramid algorithm. Sci TechnolEngI 2007; 22: 5818–5822.
  16. Li M and Dong Y. Image fusion algorithm based on contrast pyramid and application. In: International Conference on Mechatronic Sciences, Electric Engineering and Computer, 2014, pp.1342–1345. Shengyang, CN: IEEE.
  17. Li C, Li J and Fu B. Magnitude-phase of quaternion wavelet transform for texture representation using multilevel copula. IEEE Signal Process Lett 2013; 20: 799–802.
  18. Xydeas CS and Petrovic V. Objective image fusion performance measure. Electron Lett 2000; 36: 308–309.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2022-04-30

Issue

Volume 9, Issue 2, March-April-2022

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
V. Supraja, K. Swetha, E. Haritha, K. Sumalatha, G. Chandrika, " Medical Image Fusion Multi Model Based on Quaternion Wavelet Transform, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 2, pp.348-357, March-April-2022. Available at doi : https://doi.org/10.32628/IJSRSET1229264