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Implementation and Comparative Study of Image Fusion Methods in Frequency Domain

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Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 43))

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Abstract

Complementary multi-focus and/or multi-model data from two or more different images are combined into one new image is called Image fusion. The main objective is to decrease vagueness and minimizes redundancy in the output while enhancing correlate information specific to a task. Medical images coming from different resources may often give different data. So, it is challenging task to merge two or more medical images. The fused images are very useful in medical diagnosis. In this paper, image fusion has been performed in discrete wavelet transform (DWT) and Contourlet transform (CNT). As a fusion rule, spatial techniques like Averaging, Maximum Selection and PCA is used. Experiments are performed on CT and MRI medical images. For evaluation and comparative analysis of methods, a set of standard performance measures are used. This paper’s results show that, the Contourlet method gives a good performance in medical image fusion, because it provides parabolic scaling and vanishing moments.

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References

  1. Wu, T., Wu, X.-J., Luo, X.-Q.: A study on fusion of different resolution images. Int. Workshop Inf. Electron. Eng. (IWIEE) 29, 3980–3985 (2012) (Procedia Engineering, Elsevier)

    Google Scholar 

  2. Sapkal, R.J., Kulkarni, S.M.: Image fusion based on wavelet transform for medical application. Int. J. Eng. Res. Appl. (IJERA) 2(5), 624–627 (2012). ISSN: 2248–9622

    Google Scholar 

  3. Liu, S., Wang, M., Fang, Y.: A contourlet transform based fusion algorithm for nighttime driving image. In: Wang, L., et al. (eds.) FSKD, LNAI 4223, pp. 491–500. ©Springer, Berlin (2006)

    Google Scholar 

  4. Shivsubramani Krishnamoorthy, K., Soman, P.: Implementation and comparative study of image fusion algorithms. Int. J. Comput. Appl. 9(2), 0975–8887 (2010)

    Google Scholar 

  5. Singh, N., Tanwar, P.: Image fusion using improved Contourlet transform technique. Int. J. Recent Technol. Eng. (IJRTE) 1(2), (2012). ISSN: 2277–3878

    Google Scholar 

  6. Brahmbhatt, K.N., Makwana, R.M.: Comparative study on image fusion methods in spatial domain. Int. J. Adv. Res. Eng. Technol. (IJARET) 4(2), (2013) ISSN 0976–6480 (Print), ISSN 0976–6499 (Online)

    Google Scholar 

  7. Na, T., Manfred, E., Yang, W.: Remote sensing image fusion with multi-wavelet transform. In: SPIE Proceedings Series, vol. 5574, pp. 14–22 (2004). ISBN 0-8194-5521-0

    Google Scholar 

  8. Lee, C.-H., Zhou, Z.-W.: Comparison of image fusion based on DCT-STD and DWT-STD. In: Proceeding of International multi Conference of Engineers and Computer Scientist, vol. I. Hong Kong, 14–16 March 2012

    Google Scholar 

  9. He, Z.H., Bystrom, M.: Reduced feature texture retrieval using Contourlet decomposition of luminance image component. In: International Conference on Communications, Circuits and Systems, pp. 878–882 (2005)

    Google Scholar 

  10. Do, M.N., Vetterli, M.: The Contourlet transform: an efficient directional multiresolution image representation. In: IEEE Transactions on Image Processing

    Google Scholar 

  11. Chen, Y., Rick, S.B.: Experimental tests of image fusion for night vision. In: 7th International Conference on Information fusion, pp. 491–498 (2005)

    Google Scholar 

  12. Chen, S., Wu, Y.: Image fusion based on Contourlet transform and fuzzy logic. In: The National Natural Science Foundation of China (No. 60872065), 978-1-4244-4131-0/09

    Google Scholar 

  13. Deshmukh, M., Bhosale, U.: Image fusion and image quality assessment of fused images. Int. J. Image Process. (IJIP) 4(5)

    Google Scholar 

  14. Shulei, W.U., Zhizhong, Z.H.A.O., Huandong, C.H.E.N., Chunhui, S.O.N.G., Minjia, L.I.: An improved algorithm of remote sensing image fusion based on wavelet transform. J. Comput. Inf. Syst. 8(20), 8621–8628 (2012)

    Google Scholar 

  15. Prakash, C., Rajkumar, S., Chandra Mouli, P.V.S.S.R.: Medical image fusion based on redundancy DWT and Mamdani type min-sum mean-of-max techniques with quantitative analysis. In: International Conference on Recent Advances in Computing and Software Systems (2012)

    Google Scholar 

  16. Cao, W., Li, B., Zhang: A remote sensing image fusion method based on PCA and wavelet packet transform. In: IEEE International Conference Neural Networks and Signal Processing, pp. 976–981

    Google Scholar 

  17. Guest editorial. Image fusion: Advances in the state of the art. Inf. Fus. 8, 114–118 (2007) (Elsevier)

    Google Scholar 

  18. Sabari Banu, R.: Medical Image Fusion by the analysis of Pixel Level Multi-sensor Using Discrete Wavelet Transform. In: Proceedings of the National Conference on Emerging Trends in Computing Science, pp. 291–297 (2011)

    Google Scholar 

  19. Rahman, S.M.M., Ahmad, M.O., Swamy, M.N.S.: Contrast-based fusion of noisy images using discrete wavelet transform. J. Image Process. 4(5), 374–384 (2010)

    Article  MathSciNet  Google Scholar 

  20. Zhong, Z.: Investigations on image fusion. PhD Thesis, University of Lehigh, May 1999

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, CHARUSET, Changa, Gujarat, India

    Keyur N. Brahmbhatt

  2. Department of Computer Engineering, ADIT, V.V. Nagar, Gujarat, India

    Ramji M. Makwana

Authors
  1. Keyur N. Brahmbhatt
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  2. Ramji M. Makwana
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Corresponding author

Correspondence to Keyur N. Brahmbhatt .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

  2. Dept. of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

    Durga Prasad Mohapatra

  3. Computer Science & Engineering, University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

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Brahmbhatt, K.N., Makwana, R.M. (2016). Implementation and Comparative Study of Image Fusion Methods in Frequency Domain. In: Nagar, A., Mohapatra, D., Chaki, N. (eds) Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Smart Innovation, Systems and Technologies, vol 43. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2538-6_28

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  • DOI: https://doi.org/10.1007/978-81-322-2538-6_28

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2537-9

  • Online ISBN: 978-81-322-2538-6

  • eBook Packages: EngineeringEngineering (R0)

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