Skip to main content
Springer Nature Link
Log in

Detail-enhanced multimodality medical image fusion based on gradient minimization smoothing filter and shearing filter

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

In this paper, a detail-enhanced multimodality medical image fusion algorithm is proposed by using proposed multi-scale joint decomposition framework (MJDF) and shearing filter (SF). The MJDF constructed with gradient minimization smoothing filter (GMSF) and Gaussian low-pass filter (GLF) is used to decompose source images into low-pass layers, edge layers, and detail layers at multiple scales. In order to highlight the detail information in the fused image, the edge layer and the detail layer in each scale are weighted combined into a detail-enhanced layer. As directional filter is effective in capturing salient information, so SF is applied to the detail-enhanced layer to extract geometrical features and obtain directional coefficients. Visual saliency map-based fusion rule is designed for fusing low-pass layers, and the sum of standard deviation is used as activity level measurement for directional coefficients fusion. The final fusion result is obtained by synthesizing the fused low-pass layers and directional coefficients. Experimental results show that the proposed method with shift-invariance, directional selectivity, and detail-enhanced property is efficient in preserving and enhancing detail information of multimodality medical images.

The detailed implementation of the proposed medical image fusion algorithm.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Singh R, Khare A (2014) Fusion of multimodal medical images using Daubechies complex wavelet transform—a multiresolution approach. Inf Fusion 19:49–60. https://doi.org/10.1016/j.inffus.2012.09.005

    Article  Google Scholar 

  2. Petrovic VS, Xydeas CS (2004) Gradient-based multiresolution image fusion. IEEE Trans Image Process 13(2):228–237. https://doi.org/10.1109/TIP.2004.823821

    Article  PubMed  Google Scholar 

  3. Kong W, Liu J (2013) Technique for image fusion based on nonsubsampled shearlet transform and improved pulse-coupled neural network. Opt Eng 52(1):017001. https://doi.org/10.1117/1.OE.52.1.017001

    Article  Google Scholar 

  4. Miao QG, Shi C, Xu PF, Yang M, Shi YB (2011) A novel algorithm of image fusion using shearlets. Opt Commun 284(6):1540–1547. https://doi.org/10.1016/j.optcom.2010.11.048

    Article  CAS  Google Scholar 

  5. Qu G, Zhang D, Yan P (2001) Medical image fusion by wavelet transform modulus maxima. Opt Express 9(4):184–190

    Article  Google Scholar 

  6. Liu S, Zhang T, Li H, Zhao J, Li H (2015) Medical image fusion based on nuclear norm minimization. Int J Imaging Syst Technol 25(4):310–316. https://doi.org/10.1002/ima.22145

    Article  CAS  Google Scholar 

  7. Daniel E, Anitha J, Gnanaraj J (2017) Optimum laplacian wavelet mask based medical image using hybrid cuckoo search—grey wolf optimization algorithm. Knowl-Based Syst 131:58–69

    Article  Google Scholar 

  8. Daniel E, Anitha J, Rani I (2017) Optimum spectrum mask based medical image fusion using Gray Wolf Optimization. Biomed Signal Process Control 34:36–43

    Article  Google Scholar 

  9. Burt P, Adelson E (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 31(4):532–540. https://doi.org/10.1109/TCOM.1983.1095851

    Article  Google Scholar 

  10. Li H, Manjunath BS, Mitra SK (1995) Multisensor image fusion using the wavelet transform. Graph Models Image Process 57(3):235–245. https://doi.org/10.1006/gmip.1995.1022

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Yang S, Wang M, Jiao L, Wu R, Wang Z (2010) Image fusion based on a new contourlet packet. Inf Fusion 11(2):78–84. https://doi.org/10.1016/j.inffus.2009.05.001

    Article  Google Scholar 

  13. Da Cunha AL, Zhou J, Do MN (2006) The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans Image Process 15(10):3089–3101

    Article  PubMed  Google Scholar 

  14. Easley G, Labate D, Lim WQ (2008) Sparse directional image representations using the discrete shearlet transform. Appl Comput Harmon Anal 25(1):25–46. https://doi.org/10.1016/j.acha.2007.09.003

    Article  Google Scholar 

  15. Jiang Y, Wang M (2014) Image fusion using multiscale edge-preserving decomposition based on weighted least squares filter. IET Image Process 8(3):183–190. https://doi.org/10.1049/iet-ipr.2013.0429

    Article  Google Scholar 

  16. Zhao J, Feng H, Xu Z, Li Q, Liu T (2013) Detail enhanced multi-source fusion using visual weight map extraction based on multi scale edge preserving decomposition. Opt Commun 287:45–52. https://doi.org/10.1016/j.optcom.2012.08.070

    Article  CAS  Google Scholar 

  17. Li S, Kang X, Hu J (2013) Image fusion with guided filtering. IEEE Trans Image Process 22(7):2864–2875. https://doi.org/10.1109/TIP.2013.2244222

    Article  PubMed  Google Scholar 

  18. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In Proc Int Conf Comput Vis 839–846. https://doi.org/10.1109/ICCV.1998.710815

  19. He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409

    Article  PubMed  Google Scholar 

  20. Farbman Z, Fattal R, Lischinski D, Szeliski R (2008) Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans Graph 27(3):1–10. https://doi.org/10.1145/1360612.1360666

    Article  Google Scholar 

  21. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639. https://doi.org/10.1109/34.56205

    Article  Google Scholar 

  22. Zhang Y, Tian X, Ren P (2014) An adaptive bilateral filter based framework for image denoising. Neurocomputing 140:299–316

    Article  Google Scholar 

  23. Ding X, Chen L, Zheng X, Huang Y, Zeng D (2016) Single image rain and snow removal via guided L0 smoothing filter. Multimed Tools Appl 75(5):2697–2712

    Article  Google Scholar 

  24. Xu L, Lu C, Xu Y, Jia J (2011) Image smoothing via L 0 gradient minimization. ACM Trans Graph 30(6):174

    Google Scholar 

  25. Bennett EP, Mason JL, McMillan L (2007) Multispectral bilateral video fusion. IEEE Trans Image Process 16(5):1185–1194. https://doi.org/10.1109/TIP.2007.894236

    Article  PubMed  Google Scholar 

  26. Huang W, Jing Z (2007) Evaluation of focus measures in multi-focus image fusion. Pattern Recogn Lett 28(4):493–500. https://doi.org/10.1016/j.patrec.2006.09.005

    Article  CAS  Google Scholar 

  27. Amolins K, Zhang Y, Dare P (2007) Wavelet based image fusion techniques—an introduction, review and comparison. ISPRS J Photogramm Remote Sens 62(4):249–263

    Article  Google Scholar 

  28. Selesnick IW, Baraniuk RG, Kingsbury NC (2005) The dual-tree complex wavelet transform. IEEE Signal Process Mag 22(6):123–151. https://doi.org/10.1109/MSP.2005.1550194

    Article  Google Scholar 

  29. Li S, Kwok JT, Wang Y (2002) Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. Inf Fusion 3(1):17–23. https://doi.org/10.1016/S1566-2535(01)00037-9

    Article  Google Scholar 

  30. Zhang Q, Guo BL (2009) Multifocus image fusion using the nonsubsampled contourlet transform. Signal Process 89(7):1334–1346. https://doi.org/10.1016/j.sigpro.2009.01.012

    Article  Google Scholar 

  31. Cao Y, Li S, Hu J (2011) Multi-focus image fusion by nonsubsampled shearlet transform. Proc Int Conf Image Graph 17–21. https://doi.org/10.1109/ICIG.2011.37

  32. Kumar BS (2015) Image fusion based on pixel significance using cross bilateral filter. SIViP 9(5):1193–1204. https://doi.org/10.1007/s11760-013-0556-9

    Article  Google Scholar 

  33. Wang Z, Bovik AC (2002) A universal image quality index. IEEE Signal Process Lett 9(3):81–84. https://doi.org/10.1109/97.995823

    Article  CAS  Google Scholar 

  34. Piella G, Heijmans H (2003) A new quality metric for image fusion. Proc Int Conf Image Process 173–176. https://doi.org/10.1109/ICIP.2003.1247209

  35. Xydeas CS, Petrovic V (2000) Objective image fusion performance measure. Electron Lett 36(4):308–309. https://doi.org/10.1049/el:20000267

    Article  Google Scholar 

  36. Whole Brain Atlas. http://www.med.harvard.edu/AANLIB/. Accessed 15.05.16

Download references

Author information

Authors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

    Xingbin Liu, Wenbo Mei & Huiqian Du

Authors
  1. Xingbin Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Wenbo Mei
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Huiqian Du
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xingbin Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Mei, W. & Du, H. Detail-enhanced multimodality medical image fusion based on gradient minimization smoothing filter and shearing filter. Med Biol Eng Comput 56, 1565–1578 (2018). https://doi.org/10.1007/s11517-018-1796-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-018-1796-1

Keywords