Skip to main content
SpringerLink
Log in

Medical Image Fusion Using Daubechies Complex Wavelet and Near Set

  • Chapter
  • First Online:
Transactions on Computational Science XXV

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 9030))

Abstract

Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities. It helps to improve the imaging quality and reduces the redundancy, which improves the clinical applicability of medical images for diagnosis. The idea is to improve the content of an image by fusing images of multiple modalities viz. positron emission tomography (PET), computerized tomography (CT), single-photon emission computerized tomography (SPECT), magnetic resonance imaging (MRI) etc. Registration is an important step before fusion. In general, the problem of image registration can be identified as the determination of geometric transformations between the respective source image and target image.

In this paper, we have used Daubechies wavelet and near fuzzy set for registration of multi-modal images and a new pixel-level multi-modal technique for medical image fusion based on complex wavelet and near set approach. Our proposed technique produces excellent fused images and minimizes fusion associated problems giving a high quality image, restoring almost every information of the source images. In this work, we have considered various image modalities like PET, CT, SPECT and MRI. The experimental evaluation for various benchmark images shows that the proposed fusion framework can generate excellent fused images as compared to the other state-of-the-art methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bradski, G.: Introduction. In: Rusu, R.B. (ed.) Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments. STAR, vol. 85, pp. 1–14. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  2. Maes, F., Vandermeulen, D., Suetens, P.: Medical image registration using mutual information. Proc. IEEE 91(10), 1699–1722 (2003)

    Article  Google Scholar 

  3. Pajares, G., de la Cruz, J.M.: A wavelet-based image fusion tutorial. Pattern Recogn. 37(9), 1855–1872 (2004). http://www.sciencedirect.com/science/article/pii/S0031320304001037

    Article  Google Scholar 

  4. Gonzalez, R.C., Woods, R.: Digital image processing, 3rd edn. Pearson, Upper Saddle River (2008)

    Google Scholar 

  5. Karani, M.R.B., Kekre, H.B., Sarode, T.K.: A deviant transform based approach for color image registration (2012)

    Google Scholar 

  6. Wei Feng, C.Y., Hu, B.: A subpixel color image registration algorithm using quaternion phase-only correlation. In: International Conference on Audio, Language and Image Processing (2008)

    Google Scholar 

  7. Yang, B., Jing, Z.-L., Zhao, H.-T.: Review of pixel-level image fusion. J. Shanghai Jiaotong Univ. (Sci.) 15(1), 6–12 (2010). http://dx.doi.org/10.1007/s12204-010-7186-y

    Article  Google Scholar 

  8. Pohl, C., Van Genderen, J.L.: Review article multisensor image fusion in remote sensing: Concepts, methods and applications. Int. J. Remote Sens. 19(5), 823–854 (1998). http://www.tandfonline.com/doi/abs/10.1080/014311698215748

    Article  Google Scholar 

  9. Li, H., Manjunath, B.S., Mitra, S.K.: Multisensor image fusion using the wavelet transform. In: First International Conference on Image Processing (ICIP 1994), vol. 1, pp. 51–55, November 1994. http://vision.ece.ucsb.edu/publications/94ICIPWav.pdf

  10. Liu, Z., Blasch, E., Xue, Z., Zhao, J., Laganiere, R., Wu, W.: Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision: a comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 34(1), 94–109 (2012)

    Article  Google Scholar 

  11. Lewis, J.J., O’Callaghan, R.J., Nikolov, S.G., Bull, D.R., Canagarajah, N.: Pixel- and region-based image fusion with complex wavelets. Inf. Fusion 8(2), 119–130 (2007). http://dx.doi.org/10.1016/j.inffus.2005.09.006

    Article  Google Scholar 

  12. Montagner, J., Barra, V., Boire, J.-Y.: A geometrical approach to multiresolution management in the fusion of digital images. In: Lévy, P.P., Le Grand, B., Poulet, F., Soto, M., Darago, L., Toubiana, L., Vibert, J.-F. (eds.) VIEW 2006. LNCS, vol. 4370, pp. 121–136. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  13. Li, S., Yang, B.: Multifocus image fusion by combining curvelet and wavelet transform. Pattern Recogn. Lett. 29(9), 1295–1301 (2008). http://dx.doi.org/10.1016/j.patrec.2008.02.002

    Article  Google Scholar 

  14. Cao, Y., Li, S., Hu, J.: Multi-focus image fusion by nonsubsampled shearlet transform. In: 2011 Sixth International Conference on Image and Graphics (ICIG), pp. 17–21 (2011)

    Google Scholar 

  15. Po, D.D.-Y., Do, M.N.: Directional multiscale modeling of images using the contourlet transform. Trans. Img. Proc. 15(6), 1610–1620 (2006). http://dx.doi.org/10.1109/TIP.2006.873450

    Article  MathSciNet  Google Scholar 

  16. Li, S., Yang, B., Hu, J.: Performance comparison of different multi-resolution transforms for image fusion. Inf. Fusion 12(2), 74–84 (2011). http://dx.doi.org/10.1016/j.inffus.2010.03.002

    Article  Google Scholar 

  17. Xydeas, C., Petrovic, V.: Objective image fusion performance measure. Electron. Lett. 36(4), 308–309 (2000)

    Article  Google Scholar 

  18. Pei, Y., Zhou, H., Yu, J., Cai, G.: The improved wavelet transform based image fusion algorithm and the quality assessment. In: 2010 3rd International Congress on Image and Signal Processing (CISP), vol. 1, pp. 219–223 (2010)

    Google Scholar 

  19. Nencini, F., Garzelli, A., Baronti, S., Alparone, L.: Remote sensing image fusion using the curvelet transform. Inf. Fusion 8(2), 143–156 (2007). http://dx.doi.org/10.1016/j.inffus.2006.02.001

    Article  Google Scholar 

  20. Yang, L., Guo, B.L., Ni, W.: Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform. Neurocomput. 72(1–3), 203–211 (2008). http://dx.doi.org/10.1016/j.neucom.2008.02.025

    Article  Google Scholar 

  21. Qu, X.-B., Xie, G.-F., Yan, J.-W., Zhu, Z.-Q., Chen, B.-G.: Image fusion algorithm based on neighbors and cousins information in nonsubsampled contourlet transform domain. In: International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2007, vol. 4, pp. 1797–1802 (2007)

    Google Scholar 

  22. Singh, R., Khare, A.: Multimodal medical image fusion using daubechies complex wavelet transform. In: 2013 IEEE Conference on Information Communication Technologies (ICT), vol. 1, pp. 869–873 (2013)

    Google Scholar 

  23. Shahid, M., Gupta, S.: Image merging based on perceptual information. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 683–692. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  24. Peters, J.F.: Near sets. special theory about nearness of objects. Fundam. Inf. 75(1–4), 407–433 (2007). http://dl.acm.org/citation.cfm?id=1232299.1232323

    MATH  Google Scholar 

  25. Li, T., Wang, Y.: Multiscaled combination of mr and spect images in neuroimaging: a simplex method based variable-weight fusion. Comput. Methods Prog. Biomed. 105(1), 31–39 (2012). http://dx.doi.org/10.1016/j.cmpb.2010.07.012

    Article  Google Scholar 

  26. Bhatnagar, G., Wu, Q., Liu, Z.: Directive contrast based multimodal medical image fusion in nsct domain. IEEE Trans. Multimedia 15(5), 1014–1024 (2013)

    Article  Google Scholar 

  27. Khare, A., Tiwary, U.S., Pedrycz, W., Jeon, M.: Multilevel adaptive thresholding and shrinkage technique for denoising using daubechies complex wavelet transform. Imaging Sci. J. 58(6), 340–358 (2010). 2010–12-01T00:00:00. http://www.ingentaconnect.com/content/maney/isj/2010/00000058/00000006/art00005

    Article  Google Scholar 

  28. Lina, J.M., Mayrand, M.: Complexdaubechies wavelets. Appl. Comput. Harmonic Anal. 2, 219–229 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  29. Zhang, X.P., Desai, M.D., Peng, Y.N.: Orthogonal complex filter banks and wavelets:some properties and design. IEEE Trans. Sig. Proc. 47(4), 1039–1048 (1999)

    Article  MATH  Google Scholar 

  30. Dubois, D., Pradei, H.: Fuzzy Sets and Systems: Theory and applications. Academic Press, New York (1980)

    MATH  Google Scholar 

  31. Morvick, P.L.S.J., Shaw, J.: A fast, non-iterative and exact histogram matching algorithm. Pattern Recogn. Lett. 23, 127–135 (2002)

    Article  Google Scholar 

  32. Goshtas, A.A.: 2-D and 3-D Image Registration. Willey, Hoboken (2005)

    Google Scholar 

  33. Wang, L., Li, B., fang Tian, L.: Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients. Inf. Fusion 19, 20–28 (2014). http://www.sciencedirect.com/science/article/pii/S1566253512000346

    Article  Google Scholar 

  34. Medical image database. http://www.med.harvard.edu.aanlib

  35. Daneshvar, S., Ghassemian, H.: Mri and pet image fusion by combining ihs and retina-inspired models. Inf. Fusion 11(2), 114–123 (2010). http://dx.doi.org/10.1016/j.inffus.2009.05.003

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Calcutta, Calcutta, India

    Pubali Chatterjee, Somoballi Ghoshal, Biswajit Biswas & Kashi Nath Dey

  2. AKCSIT, University of Calcutta, Calcutta, India

    Amlan Chakrabarti

Authors
  1. Pubali Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Somoballi Ghoshal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Biswajit Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amlan Chakrabarti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kashi Nath Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Somoballi Ghoshal .

Editor information

Editors and Affiliations

  1. University of Calgary, Calgary, Alberta, Canada

    Marina L. Gavrilova

  2. Sardina Systems OÜ, Tallinn, Estonia

    C.J. Kenneth Tan

  3. Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  4. University of Calcutta, Kolkata, India

    Nabendu Chaki

  5. NIIT University, Neemrana, India

    Soharab Hossain Shaikh

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Chatterjee, P., Ghoshal, S., Biswas, B., Chakrabarti, A., Dey, K.N. (2015). Medical Image Fusion Using Daubechies Complex Wavelet and Near Set. In: Gavrilova, M., Tan, C., Saeed, K., Chaki, N., Shaikh, S. (eds) Transactions on Computational Science XXV. Lecture Notes in Computer Science(), vol 9030. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47074-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-47074-9_6

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47073-2

  • Online ISBN: 978-3-662-47074-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation